{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Facies Classification Using Various Machine Learning Methods and Result Comparison\n",
    "##### [Ryan A. Mardani](https://www.linkedin.com/in/amardani/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Facies classification is one of the most important tasks that geoscientists work on development and exploration projects. Sedimentary facies reflect particular physical, chemical, and biological condition that unit experienced during sedimentation process. To study these facies, rock samples are required. In this study, it is practiced to train various machine learning algorithms to predict facies from well log data. The dataset for this study comes from [Hugoton and Panoma Fields](http://www.kgs.ku.edu/PRS/publication/2003/ofr2003-50.pdf) in North America which was used in class exercise in The University of Kansas [(Dubois et. al, 2007)](https://www.sciencedirect.com/science/article/pii/S0098300406001956?via%3Dihub). It consists of log data of nine wells. We will use these log data to train supervised classifiers in order to predict discrete facies groups. All this implementation is based on [scikit-learn](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning) libraries. These are:<br> <font color=purple>\n",
    "1) Support vector machines (SVM)<br>\n",
    "2) Gaussian process classification (GPC)<br>\n",
    "3) Random forest classifier (RFC)<br> \n",
    "4) Multi-layer Perceptron classifier(Neural Network classifier, NNC)<br>\n",
    "5) K Nearest Neighbors Classifier (KNN)<br>\n",
    "6) Decision tree classifier (DT)<br>\n",
    "7) Logistic regression (LR)</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to all data science approaches, we will first start with data examination. Various features of these well data will be looked at. Cross plots and well view of logs can be an appropriate approach. Then data needs to be conditioned and remove incomplete parts. To make more efficient model performance, data should be scaled to zero mean and unit variance.  Dataset will be divided into training, test and blind well data. Then, various classifiers will be employed to fit the model. After that, models will be applied to test data. In the final step. we will examine the model efficiency with a blind well which was not involved in the model building process. We mainly use data description, the plotting functions and some approaches from [Brendon Hall](https://github.com/brendonhall/facies_classification/blob/master/Facies%20Classification%20-%20SVM.ipynb), and will examine various ML approaches rather than SVM. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding & Wrangling\n",
    "\n",
    "There are plenty of learning materials we have [here](http://www.people.ku.edu/~gbohling/EECS833/) for this data set. The training data stored in CSV format contains 5 wireline log measurements, two indicators and facies label in half foot interval.\n",
    "In machine learning terminology, each log measurement is a feature vector that maps a set of 'features' (the log measurements) to a class (the facies type).  We will use the pandas library to load the data into a dataframe, which provides a convenient data structure to work with well log data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import seaborn as sns\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from pandas import set_option\n",
    "set_option(\"display.max_rows\", 10)\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Facies</th>\n",
       "      <th>Formation</th>\n",
       "      <th>Well Name</th>\n",
       "      <th>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2793.0</td>\n",
       "      <td>77.450</td>\n",
       "      <td>0.664</td>\n",
       "      <td>9.900</td>\n",
       "      <td>11.915</td>\n",
       "      <td>4.600</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2793.5</td>\n",
       "      <td>78.260</td>\n",
       "      <td>0.661</td>\n",
       "      <td>14.200</td>\n",
       "      <td>12.565</td>\n",
       "      <td>4.100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>79.050</td>\n",
       "      <td>0.658</td>\n",
       "      <td>14.800</td>\n",
       "      <td>13.050</td>\n",
       "      <td>3.600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2794.5</td>\n",
       "      <td>86.100</td>\n",
       "      <td>0.655</td>\n",
       "      <td>13.900</td>\n",
       "      <td>13.115</td>\n",
       "      <td>3.500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>A1 SH</td>\n",
       "      <td>SHRIMPLIN</td>\n",
       "      <td>2795.0</td>\n",
       "      <td>74.580</td>\n",
       "      <td>0.647</td>\n",
       "      <td>13.500</td>\n",
       "      <td>13.300</td>\n",
       "      <td>3.400</td>\n",
       "      <td>1</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4144</td>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3120.5</td>\n",
       "      <td>46.719</td>\n",
       "      <td>0.947</td>\n",
       "      <td>1.828</td>\n",
       "      <td>7.254</td>\n",
       "      <td>3.617</td>\n",
       "      <td>2</td>\n",
       "      <td>0.685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4145</td>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3121.0</td>\n",
       "      <td>44.563</td>\n",
       "      <td>0.953</td>\n",
       "      <td>2.241</td>\n",
       "      <td>8.013</td>\n",
       "      <td>3.344</td>\n",
       "      <td>2</td>\n",
       "      <td>0.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4146</td>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3121.5</td>\n",
       "      <td>49.719</td>\n",
       "      <td>0.964</td>\n",
       "      <td>2.925</td>\n",
       "      <td>8.013</td>\n",
       "      <td>3.190</td>\n",
       "      <td>2</td>\n",
       "      <td>0.669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4147</td>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3122.0</td>\n",
       "      <td>51.469</td>\n",
       "      <td>0.965</td>\n",
       "      <td>3.083</td>\n",
       "      <td>7.708</td>\n",
       "      <td>3.152</td>\n",
       "      <td>2</td>\n",
       "      <td>0.661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4148</td>\n",
       "      <td>5</td>\n",
       "      <td>C LM</td>\n",
       "      <td>CHURCHMAN BIBLE</td>\n",
       "      <td>3122.5</td>\n",
       "      <td>50.031</td>\n",
       "      <td>0.970</td>\n",
       "      <td>2.609</td>\n",
       "      <td>6.668</td>\n",
       "      <td>3.295</td>\n",
       "      <td>2</td>\n",
       "      <td>0.653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4149 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Facies Formation        Well Name   Depth      GR  ILD_log10  DeltaPHI  \\\n",
       "0          3     A1 SH        SHRIMPLIN  2793.0  77.450      0.664     9.900   \n",
       "1          3     A1 SH        SHRIMPLIN  2793.5  78.260      0.661    14.200   \n",
       "2          3     A1 SH        SHRIMPLIN  2794.0  79.050      0.658    14.800   \n",
       "3          3     A1 SH        SHRIMPLIN  2794.5  86.100      0.655    13.900   \n",
       "4          3     A1 SH        SHRIMPLIN  2795.0  74.580      0.647    13.500   \n",
       "...      ...       ...              ...     ...     ...        ...       ...   \n",
       "4144       5      C LM  CHURCHMAN BIBLE  3120.5  46.719      0.947     1.828   \n",
       "4145       5      C LM  CHURCHMAN BIBLE  3121.0  44.563      0.953     2.241   \n",
       "4146       5      C LM  CHURCHMAN BIBLE  3121.5  49.719      0.964     2.925   \n",
       "4147       5      C LM  CHURCHMAN BIBLE  3122.0  51.469      0.965     3.083   \n",
       "4148       5      C LM  CHURCHMAN BIBLE  3122.5  50.031      0.970     2.609   \n",
       "\n",
       "       PHIND     PE  NM_M  RELPOS  \n",
       "0     11.915  4.600     1   1.000  \n",
       "1     12.565  4.100     1   0.979  \n",
       "2     13.050  3.600     1   0.957  \n",
       "3     13.115  3.500     1   0.936  \n",
       "4     13.300  3.400     1   0.915  \n",
       "...      ...    ...   ...     ...  \n",
       "4144   7.254  3.617     2   0.685  \n",
       "4145   8.013  3.344     2   0.677  \n",
       "4146   8.013  3.190     2   0.669  \n",
       "4147   7.708  3.152     2   0.661  \n",
       "4148   6.668  3.295     2   0.653  \n",
       "\n",
       "[4149 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data into python as dataframe \n",
    "dataset = 'facies_vectors.csv'\n",
    "training_data = pd.read_csv(dataset)\n",
    "training_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Council Grove gas reservoir is located in Kansas. From this carbonate reservoir, nine wells (with 4149 examples) are avaialble. Facies are stduied from core samples in every half foot and matched with logging data in well location. Feature variables include five from wireline log measurements and two geologic constraining variables that are derived from geologic knowledge.\n",
    "The seven variables are:<br>\n",
    "1. __GR__: this wireline logging tools measure gamma emission from formation. Good index for shale content.<br>\n",
    "2. __ILD_log10__ : this is resistivity measurment which is applicable for identification of reservoir fluid content.<br>\n",
    "3. __PE__ : photoelectric effect log can be used for lithology (mineral contet of rock) identificaiton.<br>\n",
    "4. __DeltaPHI__: Phi is porosity index in petrophysics. To measure porosity, there serval methods such as neutron and density. This is differences between them.<br>\n",
    "5. __PNHIND__: Average of neutron and density log.<br>\n",
    "6. __NM_M__ :nonmarine-marine indicator<br>\n",
    "7. __RELPOS__: relative position<br>\n",
    "\n",
    "The nine discrete facies (classes of rocks) are: \n",
    "1. __(SS)__   Nonmarine sandstone \n",
    "2. __(CSiS)__ Nonmarine coarse siltstone\n",
    "3. __(FSiS)__ Nonmarine fine siltstone \n",
    "4. __(SiSH)__ Marine siltstone and shale \n",
    "5. __(MS)__   Mudstone (limestone) \n",
    "6. __(WS)__   Wackestone (limestone)\n",
    "7. __(D)__    Dolomite\n",
    "8. __(PS)__   Packstone-grainstone (limestone)\n",
    "9. __(BS)__   Phylloid-algal bafflestone (limestone) \n",
    "\n",
    "Geologically, sometimes the boundray of facies are not clear and could show some transition.The following table lists the facies, their abbreviated labels and their approximate neighbors.\n",
    "\n",
    "Facies |Label| Adjacent Facies\n",
    ":---: | :---: |:--:\n",
    "1 |SS| 2\n",
    "2 |CSiS| 1,3\n",
    "3 |FSiS| 2\n",
    "4 |SiSh| 5\n",
    "5 |MS| 4,6\n",
    "6 |WS| 5,7\n",
    "7 |D| 6,8\n",
    "8 |PS| 6,7,9\n",
    "9 |BS| 7,8\n",
    "\n",
    "Let's clean up this dataset.  The 'Well Name' and 'Formation' columns can be turned into a categorical data type.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[SHRIMPLIN, ALEXANDER D, SHANKLE, LUKE G U, KIMZEY A, CROSS H CATTLE, NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]\n",
       "Categories (10, object): [SHRIMPLIN, ALEXANDER D, SHANKLE, LUKE G U, ..., NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data['Well Name'] = training_data['Well Name'].astype('category')\n",
    "training_data['Formation'] = training_data['Formation'].astype('category')\n",
    "training_data['Well Name'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the names of the 10 training wells in the Council Grove reservoir.  Data has been recruited into pseudo-well 'Recruit F9' to better represent facies 9, the Phylloid-algal bafflestone. \n",
    "\n",
    "Before we plot the well data, let's define a color map so the facies are represented by consistent color in all the plots in this tutorial.  We also create the abbreviated facies labels, and add those to the `facies_vectors` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00',\n",
    "       '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n",
    "\n",
    "facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS',\n",
    "                 'WS', 'D','PS', 'BS']\n",
    "#facies_color_map is a dictionary that maps facies labels to their respective colors\n",
    "facies_color_map = {}\n",
    "for ind, label in enumerate(facies_labels):\n",
    "    facies_color_map[label] = facies_colors[ind]\n",
    "\n",
    "def label_facies(row, labels):\n",
    "    return labels[ row['Facies'] -1]\n",
    "    \n",
    "training_data.loc[:,'FaciesLabels'] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Facies</th>\n",
       "      <th>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>4149.000000</td>\n",
       "      <td>4149.000000</td>\n",
       "      <td>4149.000000</td>\n",
       "      <td>4149.000000</td>\n",
       "      <td>4149.000000</td>\n",
       "      <td>4149.000000</td>\n",
       "      <td>3232.000000</td>\n",
       "      <td>4149.000000</td>\n",
       "      <td>4149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>4.503254</td>\n",
       "      <td>2906.867438</td>\n",
       "      <td>64.933985</td>\n",
       "      <td>0.659566</td>\n",
       "      <td>4.402484</td>\n",
       "      <td>13.201066</td>\n",
       "      <td>3.725014</td>\n",
       "      <td>1.518438</td>\n",
       "      <td>0.521852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.474324</td>\n",
       "      <td>133.300164</td>\n",
       "      <td>30.302530</td>\n",
       "      <td>0.252703</td>\n",
       "      <td>5.274947</td>\n",
       "      <td>7.132846</td>\n",
       "      <td>0.896152</td>\n",
       "      <td>0.499720</td>\n",
       "      <td>0.286644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2573.500000</td>\n",
       "      <td>10.149000</td>\n",
       "      <td>-0.025949</td>\n",
       "      <td>-21.832000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2821.500000</td>\n",
       "      <td>44.730000</td>\n",
       "      <td>0.498000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.277000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2932.500000</td>\n",
       "      <td>64.990000</td>\n",
       "      <td>0.639000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>3.551500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.528000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3007.000000</td>\n",
       "      <td>79.438000</td>\n",
       "      <td>0.822000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>16.050000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.769000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3138.000000</td>\n",
       "      <td>361.150000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>19.312000</td>\n",
       "      <td>84.400000</td>\n",
       "      <td>8.094000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Facies        Depth           GR    ILD_log10     DeltaPHI  \\\n",
       "count  4149.000000  4149.000000  4149.000000  4149.000000  4149.000000   \n",
       "mean      4.503254  2906.867438    64.933985     0.659566     4.402484   \n",
       "std       2.474324   133.300164    30.302530     0.252703     5.274947   \n",
       "min       1.000000  2573.500000    10.149000    -0.025949   -21.832000   \n",
       "25%       2.000000  2821.500000    44.730000     0.498000     1.600000   \n",
       "50%       4.000000  2932.500000    64.990000     0.639000     4.300000   \n",
       "75%       6.000000  3007.000000    79.438000     0.822000     7.500000   \n",
       "max       9.000000  3138.000000   361.150000     1.800000    19.312000   \n",
       "\n",
       "             PHIND           PE         NM_M       RELPOS  \n",
       "count  4149.000000  3232.000000  4149.000000  4149.000000  \n",
       "mean     13.201066     3.725014     1.518438     0.521852  \n",
       "std       7.132846     0.896152     0.499720     0.286644  \n",
       "min       0.550000     0.200000     1.000000     0.000000  \n",
       "25%       8.500000     3.100000     1.000000     0.277000  \n",
       "50%      12.020000     3.551500     2.000000     0.528000  \n",
       "75%      16.050000     4.300000     2.000000     0.769000  \n",
       "max      84.400000     8.094000     2.000000     1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a quick view of the statistical distribution of the input variables. Looking at the count values, most values have 4149 valid values except for PE, which has 3232. In this tutorial we will drop the feature vectors that don't have a valid PE entry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "PE_mask = training_data['PE'].notnull().values\n",
    "training_data = training_data[PE_mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the data from individual wells in a more familiar log plot form.  We will create plots for the five well log variables, as well as a log for facies labels.  The plots are based on the those described in Alessandro Amato del Monte's [excellent tutorial](https://github.com/seg/tutorials/tree/master/1504_Seismic_petrophysics_1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove a single well to use as a blind test later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "blind = training_data[training_data['Well Name'] == 'NOLAN']\n",
    "training_data = training_data[training_data['Well Name'] != 'NOLAN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2817, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_facies_log_plot(logs, facies_colors):\n",
    "    #make sure logs are sorted by depth\n",
    "    logs = logs.sort_values(by='Depth')\n",
    "    cmap_facies = colors.ListedColormap(\n",
    "            facies_colors[0:len(facies_colors)], 'indexed')\n",
    "    \n",
    "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
    "    \n",
    "    cluster=np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
    "    \n",
    "    f, ax = plt.subplots(nrows=1, ncols=6, figsize=(12, 6))\n",
    "    ax[0].plot(logs.GR, logs.Depth, '-g')\n",
    "    ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
    "    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.40')\n",
    "    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
    "    ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
    "    im=ax[5].imshow(cluster, interpolation='none', aspect='auto',\n",
    "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    \n",
    "    divider = make_axes_locatable(ax[5])\n",
    "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
    "    cbar=plt.colorbar(im, cax=cax)\n",
    "    cbar.set_label((5*' ').join([' SS ', 'CSiS', 'FSiS', \n",
    "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
    "                                ' PS ', ' BS ']))\n",
    "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
    "    \n",
    "    for i in range(len(ax)-1):\n",
    "        ax[i].set_ylim(ztop,zbot)\n",
    "        ax[i].invert_yaxis()\n",
    "        ax[i].grid()\n",
    "        ax[i].locator_params(axis='x', nbins=3)\n",
    "    \n",
    "    ax[0].set_xlabel(\"GR\")\n",
    "    ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
    "    ax[1].set_xlabel(\"ILD_log10\")\n",
    "    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
    "    ax[2].set_xlabel(\"DeltaPHI\")\n",
    "    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
    "    ax[3].set_xlabel(\"PHIND\")\n",
    "    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
    "    ax[4].set_xlabel(\"PE\")\n",
    "    ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
    "    ax[5].set_xlabel('Facies')\n",
    "    \n",
    "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
    "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([])\n",
    "    ax[5].set_xticklabels([])\n",
    "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Placing the log plotting code in a function will make it easy to plot the logs from multiples wells, and can be reused later to view the results when we apply the facies classification model to other wells.  The function was written to take a list of colors and facies labels as parameters.  \n",
    "\n",
    "We then show log plots for wells `SHRIMPLIN`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_facies_log_plot(\n",
    "    training_data[training_data['Well Name'] == 'SHRIMPLIN'],\n",
    "    facies_colors)\n",
    "#plt.savefig(\"SHRIMPLIN_X1\", dpi=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to individual wells, we can look at how the various facies are represented by the entire training set.  Let's plot a histogram of the number of training examples for each facies class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SS      255\n",
       "CSiS    620\n",
       "FSiS    547\n",
       "SiSh    156\n",
       "MS      170\n",
       "WS      432\n",
       "D        94\n",
       "PS      382\n",
       "BS      161\n",
       "Name: Facies, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#count the number of unique entries for each facies, sort them by\n",
    "#facies number (instead of by number of entries)\n",
    "facies_counts = training_data['Facies'].value_counts().sort_index()\n",
    "#use facies labels to index each count\n",
    "facies_counts.index = facies_labels\n",
    "\n",
    "facies_counts.plot(kind='bar',color=facies_colors, \n",
    "                   title='Facies Distribution in Dataset ')\n",
    "facies_counts\n",
    "#plt.savefig(\"F_Dist.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the distribution of examples by facies for the examples in the training set.  Dolomite (facies 7) has the fewest with 81 examples.  Depending on the performance of the classifier we are going to train, we may consider getting more examples of these facies.\n",
    "\n",
    "Crossplots are a familiar tool in the geosciences to visualize how two properties vary with rock type.  This dataset contains 5 log variables, and scatter matrix can help to quickly visualize the variation between the all the variables in the dataset.  We can employ the very useful [Seaborn library](https://stanford.edu/~mwaskom/software/seaborn/) to quickly create a nice looking scatter matrix. Each pane in the plot shows the relationship between two of the variables on the x and y axis, with each point is colored according to its facies.  The same colormap is used to represent the 9 facies.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: MatplotlibDeprecationWarning: \n",
      "The examples.directory rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2. In the future, examples will be found relative to the 'datapath' directory.\n",
      "  \n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
      "The examples.directory rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2. In the future, examples will be found relative to the 'datapath' directory.\n",
      "  self[key] = other[key]\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
      "The savefig.frameon rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
      "  self[key] = other[key]\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
      "The text.latex.unicode rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2.\n",
      "  self[key] = other[key]\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
      "The verbose.fileo rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
      "  self[key] = other[key]\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
      "The verbose.level rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
      "  self[key] = other[key]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 968.925x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#save plot display settings to change back to when done plotting with seaborn\n",
    "inline_rc = dict(mpl.rcParams)\n",
    "\n",
    "sns.set()\n",
    "sns.pairplot(training_data.drop(['Well Name','Facies','Formation','Depth','NM_M','RELPOS'],axis=1),\n",
    "             hue='FaciesLabels', palette=facies_color_map,\n",
    "             hue_order=list(reversed(facies_labels)))\n",
    "\n",
    "#switch back to default matplotlib plot style\n",
    "mpl.rcParams.update(inline_rc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Facies</th>\n",
       "      <th>Depth</th>\n",
       "      <th>GR</th>\n",
       "      <th>ILD_log10</th>\n",
       "      <th>DeltaPHI</th>\n",
       "      <th>PHIND</th>\n",
       "      <th>PE</th>\n",
       "      <th>NM_M</th>\n",
       "      <th>RELPOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Facies</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.363199</td>\n",
       "      <td>-0.335827</td>\n",
       "      <td>0.434966</td>\n",
       "      <td>-0.221680</td>\n",
       "      <td>-0.360088</td>\n",
       "      <td>0.699465</td>\n",
       "      <td>0.860088</td>\n",
       "      <td>0.065094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Depth</td>\n",
       "      <td>0.363199</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.085619</td>\n",
       "      <td>0.207171</td>\n",
       "      <td>-0.087318</td>\n",
       "      <td>-0.059062</td>\n",
       "      <td>0.285313</td>\n",
       "      <td>0.313186</td>\n",
       "      <td>-0.015109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GR</td>\n",
       "      <td>-0.335827</td>\n",
       "      <td>-0.085619</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.147170</td>\n",
       "      <td>0.182982</td>\n",
       "      <td>0.247347</td>\n",
       "      <td>-0.299145</td>\n",
       "      <td>-0.268035</td>\n",
       "      <td>-0.186145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ILD_log10</td>\n",
       "      <td>0.434966</td>\n",
       "      <td>0.207171</td>\n",
       "      <td>-0.147170</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109111</td>\n",
       "      <td>-0.520112</td>\n",
       "      <td>0.429270</td>\n",
       "      <td>0.547478</td>\n",
       "      <td>0.093388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DeltaPHI</td>\n",
       "      <td>-0.221680</td>\n",
       "      <td>-0.087318</td>\n",
       "      <td>0.182982</td>\n",
       "      <td>-0.109111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.284355</td>\n",
       "      <td>0.047548</td>\n",
       "      <td>-0.151405</td>\n",
       "      <td>0.042295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PHIND</td>\n",
       "      <td>-0.360088</td>\n",
       "      <td>-0.059062</td>\n",
       "      <td>0.247347</td>\n",
       "      <td>-0.520112</td>\n",
       "      <td>-0.284355</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.589487</td>\n",
       "      <td>-0.487452</td>\n",
       "      <td>-0.030956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PE</td>\n",
       "      <td>0.699465</td>\n",
       "      <td>0.285313</td>\n",
       "      <td>-0.299145</td>\n",
       "      <td>0.429270</td>\n",
       "      <td>0.047548</td>\n",
       "      <td>-0.589487</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.648681</td>\n",
       "      <td>0.017317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NM_M</td>\n",
       "      <td>0.860088</td>\n",
       "      <td>0.313186</td>\n",
       "      <td>-0.268035</td>\n",
       "      <td>0.547478</td>\n",
       "      <td>-0.151405</td>\n",
       "      <td>-0.487452</td>\n",
       "      <td>0.648681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RELPOS</td>\n",
       "      <td>0.065094</td>\n",
       "      <td>-0.015109</td>\n",
       "      <td>-0.186145</td>\n",
       "      <td>0.093388</td>\n",
       "      <td>0.042295</td>\n",
       "      <td>-0.030956</td>\n",
       "      <td>0.017317</td>\n",
       "      <td>0.025896</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Facies     Depth        GR  ILD_log10  DeltaPHI     PHIND  \\\n",
       "Facies     1.000000  0.363199 -0.335827   0.434966 -0.221680 -0.360088   \n",
       "Depth      0.363199  1.000000 -0.085619   0.207171 -0.087318 -0.059062   \n",
       "GR        -0.335827 -0.085619  1.000000  -0.147170  0.182982  0.247347   \n",
       "ILD_log10  0.434966  0.207171 -0.147170   1.000000 -0.109111 -0.520112   \n",
       "DeltaPHI  -0.221680 -0.087318  0.182982  -0.109111  1.000000 -0.284355   \n",
       "PHIND     -0.360088 -0.059062  0.247347  -0.520112 -0.284355  1.000000   \n",
       "PE         0.699465  0.285313 -0.299145   0.429270  0.047548 -0.589487   \n",
       "NM_M       0.860088  0.313186 -0.268035   0.547478 -0.151405 -0.487452   \n",
       "RELPOS     0.065094 -0.015109 -0.186145   0.093388  0.042295 -0.030956   \n",
       "\n",
       "                 PE      NM_M    RELPOS  \n",
       "Facies     0.699465  0.860088  0.065094  \n",
       "Depth      0.285313  0.313186 -0.015109  \n",
       "GR        -0.299145 -0.268035 -0.186145  \n",
       "ILD_log10  0.429270  0.547478  0.093388  \n",
       "DeltaPHI   0.047548 -0.151405  0.042295  \n",
       "PHIND     -0.589487 -0.487452 -0.030956  \n",
       "PE         1.000000  0.648681  0.017317  \n",
       "NM_M       0.648681  1.000000  0.025896  \n",
       "RELPOS     0.017317  0.025896  1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get correlation\n",
    "training_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 9)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize data\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(14,8))\n",
    "\n",
    "ax = sns.heatmap(training_data.corr(), annot=True, fmt ='.0%')\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "plt.setp(ax.get_yticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "ax.set_ylim(len(training_data)-2817,9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the best correlation of target label(facies) belongs to geological factor of NM_M with 86% agreement. PE, which is lithology loging tools shows 70%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Conditioning\n",
    "\n",
    "We need to select feature vaiables to perform the classification. These are the five wireline values and two geologic constraining variables. The target label as Facies need to selected as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "correct_facies_labels = training_data['Facies'].values\n",
    "feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocessing (make standard)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost all machine learning models work efficenctly when data is standardized for zero mean and unit variance. Using Scikit preprocessong module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
    "scaled_features = scaler.transform(feature_vectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocessing (Data split)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Scikit data split function, we may randomly split the training data into training and test sets. We select 20% of the data for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "        scaled_features, correct_facies_labels, test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling\n",
    "There are [several](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning) tyes of model approaches that can be used for Facies classificaiton. Explanation the basic concept behind each methods and pros/cons of them are out of scope of this study. Almost all data science book have chapters on it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the SVM classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "SVM_model = svm.SVC(C=10, gamma=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we used model parameters which is optimized by [Brendon Hall](https://github.com/brendonhall/facies_classification/blob/master/Facies%20Classification%20-%20SVM.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf', max_iter=-1,\n",
       "    probability=False, random_state=None, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVM_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting model, we can predict the result by test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat_SVM = SVM_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation\n",
    "There are sevral metrices aviable to see how model perfrom on dataset and prediction. The basics for all types of evaluation is similar; how far/close the predicted data is from actual data. \n",
    "##### Confusion matrix \n",
    "It is a 2D array of predicted and actual target label. The entries of confusion matrix C[i][j] are equal to the number of observations predicted to have facies j, but are known to have facies i.\n",
    "\n",
    "To simplify reading the confusion matrix, a function has been written to display the matrix along with facies labels and various error metrics. See the file classification_utilities in Scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
      "     True\n",
      "       SS    42    10                                              52\n",
      "     CSiS     6    83    23     1                       1         114\n",
      "     FSiS     2    19    79     2     1     1           1         105\n",
      "     SiSh                      29           6           2          37\n",
      "       MS                       3    17     5           3          28\n",
      "       WS                 2     3     7    75          11     2   100\n",
      "        D           1     2           1          22     2     1    29\n",
      "       PS                             7    12     2    53     2    76\n",
      "       BS                 1                 1           1    20    23\n",
      "\n",
      "Precision  0.84  0.73  0.74  0.76  0.52  0.75  0.92  0.72  0.80  0.75\n",
      "   Recall  0.81  0.73  0.75  0.78  0.61  0.75  0.76  0.70  0.87  0.74\n",
      "       F1  0.82  0.73  0.75  0.77  0.56  0.75  0.83  0.71  0.83  0.75\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from classification_utilities import display_cm, display_adj_cm\n",
    "\n",
    "conf_SVM = confusion_matrix(y_test, yhat_SVM)\n",
    "display_cm(conf_SVM, facies_labels, hide_zeros=True, display_metrics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix has rows and columns. The rows correspond to actual facies labels and columns show model prediction results. Let's look at the first column and row. SS is shown with 42 true predictions while 6 members of SS are predicted as CSiS and 2 as FSiS. As high value as a possible outcome of the model in diagonal of the matrix, as good as model prediction perfromance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### F1 Score\n",
    "\n",
    "Precision and recall can be computed easily using the confusion matrix. Precision is the probability that given a classification result for a sample, the sample actually belongs to that class. Recall is the probability that a sample will be correctly classified for a given class.<br>\n",
    "Let's look at the results and consider facies SS. In test set, if a sample was labeled SS the probability the sample was correct is 0.84 (precision). If we know a sample has facies SS, then the probability it will be correctly labeled by the classifier is 0.81 (recall). It is desirable to have high values for both precision and recall, but often when an algorithm is tuned to increase one, the other decreases. The F1 score combines both to give a single measure of relevancy of the classifier results.\n",
    "\n",
    "These results can help guide intuition for how to improve the classifier results. For example, for a sample with facies MS or mudstone, it is only classified correctly 61% of the time (recall). Perhaps this could be improved by introducing more training samples. Sample quality could also play a role. Facies BS or bafflestone has the best F1 score and relatively few training examples. But this data was handpicked from other wells to provide training examples to identify this facies.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Jaccard Index\n",
    "Jaccard similarity coefficient is defined as size of intersection of real (y) and predicted values (yp) divided by size of the union of those two labels.  $ J(y,y_p )=  (|y∩y_p |)/(|y∪y_p |) $. <br> \n",
    "For example, for a set of size 10 samples with 8 correct prediction, this index will be 0.66."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Jaccard index: 0.745\n",
      "SVM F1-score: 0.746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "jSVM = jaccard_similarity_score(y_test, yhat_SVM)\n",
    "f1SVM = f1_score(y_test, yhat_SVM, average='weighted')\n",
    "print(\"SVM Jaccard index: %.3f\" % jSVM )\n",
    "print(\"SVM F1-score: %.3f\" % f1SVM  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the GaussianProcessClassifier classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPC Jaccard index: 0.663\n",
      "GPC F1-score: 0.663\n",
      "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
      "     True\n",
      "       SS    34    16     2                                        52\n",
      "     CSiS     8    75    30                 1                     114\n",
      "     FSiS     1    28    72     1     1     1           1         105\n",
      "     SiSh                      25          10           2          37\n",
      "       MS                            10    12     1     5          28\n",
      "       WS                 1     3     7    71          16     2   100\n",
      "        D           1                       1    20     5     2    29\n",
      "       PS                       1     2    20     2    46     5    76\n",
      "       BS                                   1           1    21    23\n",
      "\n",
      "Precision  0.79  0.62  0.69  0.83  0.50  0.61  0.87  0.61  0.70  0.67\n",
      "   Recall  0.65  0.66  0.69  0.68  0.36  0.71  0.69  0.61  0.91  0.66\n",
      "       F1  0.72  0.64  0.69  0.75  0.42  0.65  0.77  0.61  0.79  0.66\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "\n",
    "GPC_model = GaussianProcessClassifier().fit(X_train, y_train)\n",
    "\n",
    "yhat_GPC = GPC_model.predict(X_test)\n",
    "jGPC = jaccard_similarity_score(y_test, yhat_GPC)\n",
    "f1GPC = f1_score(y_test, yhat_GPC, average='weighted')\n",
    "print(\"GPC Jaccard index: %.3f\" % jGPC)\n",
    "print(\"GPC F1-score: %.3f\" % jGPC )\n",
    "\n",
    "conf_GPC = confusion_matrix(y_test, yhat_GPC)\n",
    "display_cm(conf_GPC, facies_labels, hide_zeros=True, display_metrics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFC Jaccard index: 0.693\n",
      "RFC F1-score: 0.695\n",
      "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
      "     True\n",
      "       SS    35    15     2                                        52\n",
      "     CSiS     4    89    20                 1                     114\n",
      "     FSiS     1    29    70           1     2           2         105\n",
      "     SiSh                      23     2     9           3          37\n",
      "       MS                       2    12     7           7          28\n",
      "       WS                 1     2     7    75          13     2   100\n",
      "        D           1                 3     1    19     4     1    29\n",
      "       PS                             4    24     1    47          76\n",
      "       BS                                   1           1    21    23\n",
      "\n",
      "Precision  0.88  0.66  0.75  0.85  0.41  0.62  0.95  0.61  0.88  0.71\n",
      "   Recall  0.67  0.78  0.67  0.62  0.43  0.75  0.66  0.62  0.91  0.69\n",
      "       F1  0.76  0.72  0.71  0.72  0.42  0.68  0.78  0.61  0.89  0.69\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "RFC_model = RandomForestClassifier(max_depth=12, n_estimators=20, max_features=6).fit(X_train, y_train)\n",
    "\n",
    "\n",
    "yhat_RFC = RFC_model.predict(X_test)\n",
    "\n",
    "jRFC  = jaccard_similarity_score(y_test, yhat_RFC)\n",
    "f1RFC = f1_score(y_test, yhat_RFC, average='weighted')\n",
    "\n",
    "print(\"RFC Jaccard index: %.3f\" % jRFC )\n",
    "print(\"RFC F1-score: %.3f\" % f1RFC)\n",
    "\n",
    "conf_RFC = confusion_matrix(y_test, yhat_RFC)\n",
    "display_cm(conf_RFC, facies_labels, hide_zeros=True, display_metrics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Neural Network Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NNC Jaccard index: 0.723\n",
      "NNC F1-score: 0.724\n",
      "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
      "     True\n",
      "       SS    37    13     2                                        52\n",
      "     CSiS     4    85    24                             1         114\n",
      "     FSiS     2    20    80     1           2                     105\n",
      "     SiSh                      27     1     6           3          37\n",
      "       MS                 1     2    13     7           5          28\n",
      "       WS                 3     1     4    71          20     1   100\n",
      "        D           1                 1          21     4     2    29\n",
      "       PS                             6    12     1    53     4    76\n",
      "       BS                                   2                21    23\n",
      "\n",
      "Precision  0.86  0.71  0.73  0.87  0.52  0.71  0.95  0.62  0.75  0.73\n",
      "   Recall  0.71  0.75  0.76  0.73  0.46  0.71  0.72  0.70  0.91  0.72\n",
      "       F1  0.78  0.73  0.74  0.79  0.49  0.71  0.82  0.65  0.82  0.72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "\n",
    "NNC_model = MLPClassifier(alpha=0.001, max_iter=1000, learning_rate_init=0.001, \n",
    "                          solver='adam', batch_size=10, hidden_layer_sizes=200 ).fit(X_train, y_train)\n",
    "\n",
    "yhat_NNC = NNC_model.predict(X_test)\n",
    "jNNC  =  jaccard_similarity_score(y_test, yhat_NNC)\n",
    "f1NNC =  f1_score(y_test, yhat_NNC, average='weighted')\n",
    "\n",
    "print(\"NNC Jaccard index: %.3f\" %jNNC)\n",
    "print(\"NNC F1-score: %.3f\" %f1NNC )\n",
    "\n",
    "conf_NNC = confusion_matrix(y_test, yhat_NNC)\n",
    "display_cm(conf_NNC, facies_labels, hide_zeros=True, display_metrics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the K Neighbor Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Jaccard index: 0.716\n",
      "KNN F1-score: 0.717\n",
      "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
      "     True\n",
      "       SS    40    10     2                                        52\n",
      "     CSiS     5    78    30     1                                 114\n",
      "     FSiS     2    21    77     1     1     1           2         105\n",
      "     SiSh                      27     1     7           2          37\n",
      "       MS                       4    15     5           4          28\n",
      "       WS                 1     3     5    73          15     3   100\n",
      "        D           1                 1          24     2     1    29\n",
      "       PS                            10    14     1    48     3    76\n",
      "       BS                                               1    22    23\n",
      "\n",
      "Precision  0.85  0.71  0.70  0.75  0.45  0.73  0.96  0.65  0.76  0.72\n",
      "   Recall  0.77  0.68  0.73  0.73  0.54  0.73  0.83  0.63  0.96  0.72\n",
      "       F1  0.81  0.70  0.72  0.74  0.49  0.73  0.89  0.64  0.85  0.72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# to chose the best k value we may run in range of valus \n",
    "Ks =15\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "ConfusMtx = [];\n",
    "\n",
    "for n in range(1,Ks):\n",
    "    KNN_model = KNeighborsClassifier(n_neighbors=n).fit(X_train, y_train)\n",
    "    yhat = KNN_model.predict(X_test)\n",
    "    \n",
    "    mean_acc[n-1]= np.mean(yhat==y_test);\n",
    "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "k = 5    #it seems 5 is good enough\n",
    "KNN_model = KNeighborsClassifier(n_neighbors=k ,leaf_size=50, p=1,  weights='distance' ).fit(X_train, y_train)\n",
    "KNN_model\n",
    "#plot\n",
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Nabors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "yhat_KNN = KNN_model.predict(X_test)\n",
    "\n",
    "jKNN  = jaccard_similarity_score(y_test, yhat_KNN)\n",
    "f1KNN = f1_score(y_test, yhat_KNN, average='weighted')\n",
    "print(\"KNN Jaccard index: %.3f\" % jKNN )\n",
    "print(\"KNN F1-score: %.3f\" % f1KNN  )\n",
    "\n",
    "conf_KNN = confusion_matrix(y_test, yhat_KNN)\n",
    "display_cm(conf_KNN, facies_labels, hide_zeros=True, display_metrics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Decision Tree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DT Jaccard index: 0.598\n",
      "DT F1-score: 0.600\n",
      "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
      "     True\n",
      "       SS    39    12     1                                        52\n",
      "     CSiS     7    73    32     1     1                           114\n",
      "     FSiS     1    33    65     1     1     2           2         105\n",
      "     SiSh                      19     3    10           4     1    37\n",
      "       MS           1           1    13     8     1     4          28\n",
      "       WS                 2     4    11    55          23     5   100\n",
      "        D           1           2     4     2    14     4     2    29\n",
      "       PS                       1     6    25     2    40     2    76\n",
      "       BS                                   1           3    19    23\n",
      "\n",
      "Precision  0.83  0.61  0.65  0.66  0.33  0.53  0.82  0.50  0.66  0.61\n",
      "   Recall  0.75  0.64  0.62  0.51  0.46  0.55  0.48  0.53  0.83  0.60\n",
      "       F1  0.79  0.62  0.63  0.58  0.39  0.54  0.61  0.51  0.73  0.60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "DT_model = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 25, min_samples_split=3 )\n",
    "DT_model.fit(X_train,y_train)\n",
    "DT_model\n",
    "\n",
    "yhat_DT = DT_model.predict(X_test)\n",
    "\n",
    "jDT  = jaccard_similarity_score(y_test, yhat_DT)\n",
    "f1DT = f1_score(y_test, yhat_DT, average='weighted')\n",
    "print(\"DT Jaccard index: %.3f\" % jDT )\n",
    "print(\"DT F1-score: %.3f\" % f1DT )\n",
    "\n",
    "conf_DT = confusion_matrix(y_test, yhat_DT)\n",
    "display_cm(conf_DT, facies_labels, hide_zeros=True, display_metrics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Logistic Regression classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Jaccard index: 0.603\n",
      "LR F1-score: 0.589\n",
      "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
      "     True\n",
      "       SS    37    15                                              52\n",
      "     CSiS     7    82    23                 1           1         114\n",
      "     FSiS     2    41    55     2     1     2           2         105\n",
      "     SiSh                      16          20                 1    37\n",
      "       MS                       2     1    19     1     5          28\n",
      "       WS                 1     3          76     2    15     3   100\n",
      "        D                 1     3           3    15     6     1    29\n",
      "       PS                       1     3    22     2    40     8    76\n",
      "       BS                                   1           4    18    23\n",
      "\n",
      "Precision  0.80  0.59  0.69  0.59  0.20  0.53  0.75  0.55  0.58  0.60\n",
      "   Recall  0.71  0.72  0.52  0.43  0.04  0.76  0.52  0.53  0.78  0.60\n",
      "       F1  0.76  0.65  0.59  0.50  0.06  0.62  0.61  0.54  0.67  0.59\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "LR_model = LogisticRegression(C=1).fit(X_train,y_train)\n",
    "LR_model\n",
    "\n",
    "yhat_LR = LR_model.predict(X_test)\n",
    "\n",
    "jLR  = jaccard_similarity_score(y_test, yhat_LR)\n",
    "f1LR = f1_score(y_test, yhat_LR, average='weighted')\n",
    "print(\"LR Jaccard index: %.3f\" % jLR )\n",
    "print(\"LR F1-score: %.3f\" % f1LR )\n",
    "\n",
    "conf_LR = confusion_matrix(y_test, yhat_LR)\n",
    "display_cm(conf_LR, facies_labels, hide_zeros=True, display_metrics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Table E1, Create dataframe of model evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model type</th>\n",
       "      <th>Jaccard index</th>\n",
       "      <th>F1-Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SVM</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>GPC</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>RFC</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NNC</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>KNN</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>DT</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>LR</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model type  Jaccard index  F1-Score\n",
       "0        SVM           0.74      0.75\n",
       "1        GPC           0.66      0.66\n",
       "2        RFC           0.69      0.69\n",
       "3        NNC           0.72      0.72\n",
       "4        KNN           0.72      0.72\n",
       "5         DT           0.60      0.60\n",
       "6         LR           0.60      0.59"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dictionary with calculated errors as variables\n",
    "data_frame1 ={'Model type': ['SVM', 'GPC', 'RFC', 'NNC', 'KNN', 'DT', 'LR'],\n",
    "       'Jaccard index': [jSVM, jGPC, jRFC, jNNC, jKNN, jDT, jLR],\n",
    "       'F1-Score': [f1SVM, f1GPC, f1RFC, f1NNC, f1KNN, f1DT, f1LR]\n",
    "            }\n",
    "df1 = pd.DataFrame(data_frame1, columns = ['Model type','Jaccard index','F1-Score' ] )\n",
    "df1.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying the classification model to the blind data\n",
    "\n",
    "As we selected one well out of the training data from traning and model fitting process, it can be good estimate to see how models work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's store target labet from blind well in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_blind = blind['Facies'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling the same features for prediction, by dropping some of the columns and making a new dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "blind_features = blind.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can transform this with the scaler we made before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_blind = scaler.transform(blind_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's a simple matter of making a prediction and storing it back in the dataframe:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predicting facies for blind well by all models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SVM\n",
    "yhat_blind_SVM = SVM_model.predict(X_blind)\n",
    "blind['SVM_Pred'] = yhat_blind_SVM\n",
    "\n",
    "#GPC\n",
    "yhat_blind_GPC = GPC_model.predict(X_blind)\n",
    "blind['GPC_Pred'] = yhat_blind_GPC\n",
    "\n",
    "#RFC\n",
    "yhat_blind_RFC = RFC_model.predict(X_blind)\n",
    "blind['RFC_Pred'] = yhat_blind_RFC\n",
    "\n",
    "#NNC\n",
    "yhat_blind_NNC = NNC_model.predict(X_blind)\n",
    "blind['NNC_Pred'] = yhat_blind_NNC\n",
    "\n",
    "#KNN\n",
    "yhat_blind_KNN = KNN_model.predict(X_blind)\n",
    "blind['KNN_Pred'] = yhat_blind_KNN\n",
    "\n",
    "#DT\n",
    "yhat_blind_DT = DT_model.predict(X_blind)\n",
    "blind['DT_Pred'] = yhat_blind_DT\n",
    "\n",
    "#LR\n",
    "yhat_blind_LR = LR_model.predict(X_blind)\n",
    "blind['LR_Pred'] = yhat_blind_LR\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's see how prediction works on blind data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1439: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n",
      "C:\\Users\\Alireza\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  'and multiclass classification tasks.', DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#SVM\n",
    "jSVM_b =  jaccard_similarity_score(y_blind, yhat_blind_SVM)\n",
    "f1SVM_b = f1_score(y_blind, yhat_blind_SVM, average='weighted')\n",
    "# print(\"SVM Jaccard index for blind well prediction: %.3f\" %jSVM_b)\n",
    "# print(\"SVM F1-score for blind well prediction: %.3f\" %f1SVM_b )\n",
    "\n",
    "#GPC\n",
    "jGPC_b =jaccard_similarity_score(y_blind, yhat_blind_GPC)\n",
    "f1GPC_b =f1_score(y_blind, yhat_blind_GPC, average='weighted')\n",
    "# print(\"GPC Jaccard index for blind well prediction: %.3f\" %jGPC_b )\n",
    "# print(\"GPC F1-score for blind well prediction: %.3f\" % f1GPC_b)\n",
    "\n",
    "#RFC\n",
    "jRFC_b =jaccard_similarity_score(y_blind, yhat_blind_RFC)\n",
    "f1RFC_b=f1_score(y_blind, yhat_blind_RFC, average='weighted')\n",
    "# print(\"RFC Jaccard index for blind well prediction: %.3f\" % jRFC_b)\n",
    "# print(\"RFC F1-score for blind well prediction: %.3f\" % f1RFC_b)\n",
    "\n",
    "#NNC\n",
    "jNNC_b  =jaccard_similarity_score(y_blind, yhat_blind_NNC)\n",
    "f1NNC_b =f1_score(y_blind, yhat_blind_NNC, average='weighted')\n",
    "# print(\"NNC Jaccard index for blind well prediction: %.3f\" %jNNC_b )\n",
    "# print(\"NNC F1-score for blind well prediction: %.3f\" %f1NNC_b )\n",
    "\n",
    "#KNN\n",
    "jKNN_b = jaccard_similarity_score(y_blind, yhat_blind_KNN)\n",
    "f1KNN_b = f1_score(y_blind, yhat_blind_KNN, average='weighted')\n",
    "# print(\"KNN Jaccard index for blind well prediction: %.3f\" % jKNN_b)\n",
    "# print(\"KNN F1-score for blind well prediction: %.3f\" %f1KNN_b  )\n",
    "\n",
    "#DT\n",
    "jDT_b = jaccard_similarity_score(y_blind, yhat_blind_DT)\n",
    "f1DT_b =f1_score(y_blind, yhat_blind_DT, average='weighted')\n",
    "# print(\"DT Jaccard index for blind well prediction: %.3f\" % jDT_b)\n",
    "# print(\"DT F1-score for blind well prediction: %.3f\" % f1DT_b )\n",
    "\n",
    "#LR\n",
    "jLR_b  = jaccard_similarity_score(y_blind, yhat_blind_LR)\n",
    "f1LR_b = f1_score(y_blind, yhat_blind_LR, average='weighted')\n",
    "# print(\"LR Jaccard index for blind well prediction: %.3f\" % jLR_b)\n",
    "# print(\"LR F1-score for blind well prediction: %.3f\" %f1LR_b )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Table E2, Create dataframe of model evaluation for blind well performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model type</th>\n",
       "      <th>Jaccard index</th>\n",
       "      <th>F1-Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SVM</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>GPC</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>RFC</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NNC</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>KNN</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>DT</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>LR</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model type  Jaccard index  F1-Score\n",
       "0        SVM           0.46      0.49\n",
       "1        GPC           0.47      0.50\n",
       "2        RFC           0.49      0.52\n",
       "3        NNC           0.56      0.58\n",
       "4        KNN           0.46      0.51\n",
       "5         DT           0.41      0.45\n",
       "6         LR           0.48      0.50"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame2 ={'Model type': ['SVM', 'GPC', 'RFC', 'NNC', 'KNN', 'DT', 'LR'],\n",
    "       'Jaccard index': [jSVM_b, jGPC_b, jRFC_b, jNNC_b, jKNN_b, jDT_b, jLR_b],\n",
    "       'F1-Score': [f1SVM_b, f1GPC_b, f1RFC_b, f1NNC_b, f1KNN_b, f1DT_b, f1LR_b]\n",
    "            }\n",
    "df2 = pd.DataFrame(data_frame2, columns = ['Model type','Jaccard index','F1-Score' ] )\n",
    "df2.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot one of the model's prediction performance with blind well data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_facies_plot(logs, compadre, facies_colors):\n",
    "    #make sure logs are sorted by depth\n",
    "    logs = logs.sort_values(by='Depth')\n",
    "    cmap_facies = colors.ListedColormap(\n",
    "            facies_colors[0:len(facies_colors)], 'indexed')\n",
    "    \n",
    "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
    "    \n",
    "    cluster1 = np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
    "    cluster2 = np.repeat(np.expand_dims(logs[compadre].values,1), 100, 1)\n",
    "    \n",
    "    f, ax = plt.subplots(nrows=1, ncols=7, figsize=(12, 6))\n",
    "    ax[0].plot(logs.GR, logs.Depth, '-g')\n",
    "    ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
    "    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')\n",
    "    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
    "    ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
    "    im1 = ax[5].imshow(cluster1, interpolation='none', aspect='auto',\n",
    "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    im2 = ax[6].imshow(cluster2, interpolation='none', aspect='auto',\n",
    "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    \n",
    "    divider = make_axes_locatable(ax[6])\n",
    "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
    "    cbar=plt.colorbar(im2, cax=cax)\n",
    "    cbar.set_label((5*' ').join([' SS ', 'CSiS', 'FSiS', \n",
    "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
    "                                ' PS ', ' BS ']))\n",
    "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
    "    \n",
    "    for i in range(len(ax)-2):\n",
    "        ax[i].set_ylim(ztop,zbot)\n",
    "        ax[i].invert_yaxis()\n",
    "        ax[i].grid()\n",
    "        ax[i].locator_params(axis='x', nbins=3)\n",
    "    \n",
    "    ax[0].set_xlabel(\"GR\")\n",
    "    ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
    "    ax[1].set_xlabel(\"ILD_log10\")\n",
    "    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
    "    ax[2].set_xlabel(\"DeltaPHI\")\n",
    "    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
    "    ax[3].set_xlabel(\"PHIND\")\n",
    "    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
    "    ax[4].set_xlabel(\"PE\")\n",
    "    ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
    "    ax[5].set_xlabel('Facies')\n",
    "    ax[6].set_xlabel(compadre)\n",
    "    \n",
    "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
    "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([]); ax[6].set_yticklabels([])\n",
    "    ax[5].set_xticklabels([])\n",
    "    ax[6].set_xticklabels([])\n",
    "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's plot KNN result\n",
    "compare_facies_plot(blind, 'NNC_Pred', facies_colors)\n",
    "#plt.savefig(\"KNN.png\", dpi=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a section to compare various model performance in facies prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_all_facies(logs, Pred1, Pred2, Pred3, Pred4, Pred5, Pred6, Pred7, facies_colors):\n",
    "    #make sure logs are sorted by depth\n",
    "    logs = logs.sort_values(by='Depth')\n",
    "    cmap_facies = colors.ListedColormap(facies_colors[0:len(facies_colors)], 'indexed')\n",
    "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
    "    \n",
    "    cluster1 = np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
    "    cluster2 = np.repeat(np.expand_dims(logs[Pred1].values,1), 100, 1)\n",
    "    cluster3 = np.repeat(np.expand_dims(logs[Pred2].values,1), 100, 1)\n",
    "    cluster4 = np.repeat(np.expand_dims(logs[Pred3].values,1), 100, 1)\n",
    "    cluster5 = np.repeat(np.expand_dims(logs[Pred4].values,1), 100, 1)\n",
    "    cluster6 = np.repeat(np.expand_dims(logs[Pred5].values,1), 100, 1)\n",
    "    cluster7 = np.repeat(np.expand_dims(logs[Pred6].values,1), 100, 1)\n",
    "    cluster8 = np.repeat(np.expand_dims(logs[Pred7].values,1), 100, 1)\n",
    "\n",
    "   \n",
    "    f, ax = plt.subplots(nrows=1, ncols=8, figsize=(12, 6))\n",
    "\n",
    "    im1 = ax[0].imshow(cluster1, interpolation='none', aspect='auto',\n",
    "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    im2 = ax[1].imshow(cluster2, interpolation='none', aspect='auto',\n",
    "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    im3 = ax[2].imshow(cluster3, interpolation='none', aspect='auto',\n",
    "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    im4 = ax[3].imshow(cluster4, interpolation='none', aspect='auto',\n",
    "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    im5 = ax[4].imshow(cluster5, interpolation='none', aspect='auto',\n",
    "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    im6 = ax[5].imshow(cluster6, interpolation='none', aspect='auto',\n",
    "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    im7 = ax[6].imshow(cluster7, interpolation='none', aspect='auto',\n",
    "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
    "    im8 = ax[7].imshow(cluster8, interpolation='none', aspect='auto',\n",
    "                       cmap=cmap_facies,vmin=1,vmax=9)\n",
    "       \n",
    "    \n",
    "    divider = make_axes_locatable(ax[7])\n",
    "    cax = divider.append_axes(\"right\", size=\"10%\", pad=0.05)\n",
    "    cbar=plt.colorbar(im8, cax=cax)\n",
    "    cbar.set_label((5*' ').join([' SS ', 'CSiS', 'FSiS', \n",
    "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
    "                                ' PS ', ' BS ']))\n",
    "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
    "    \n",
    "    for i in range(len(ax)-8):\n",
    "        ax[i].set_ylim(ztop,zbot)\n",
    "        ax[i].invert_yaxis()\n",
    "        ax[i].grid()\n",
    "        ax[i].locator_params(axis='x', nbins=2)\n",
    "    \n",
    "    ax[0].set_xlabel('Facies'); ax[1].set_xlabel(Pred1); ax[2].set_xlabel(Pred2)\n",
    "    ax[3].set_xlabel(Pred3); ax[4].set_xlabel(Pred4); ax[5].set_xlabel(Pred5)\n",
    "    ax[6].set_xlabel(Pred6); ax[7].set_xlabel(Pred7)\n",
    "    \n",
    "    #ax[0].set_yticklabels([]) ;\n",
    "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([]) \n",
    "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([]); ax[6].set_yticklabels([])\n",
    "    ax[7].set_yticklabels([])\n",
    "    \n",
    "    ax[0].set_xticklabels([]); ax[1].set_xticklabels([]); ax[2].set_xticklabels([])\n",
    "    ax[3].set_xticklabels([]); ax[4].set_xticklabels([]); ax[5].set_xticklabels([])\n",
    "    ax[6].set_xticklabels([]); ax[7].set_xticklabels([])\n",
    "\n",
    "    f.suptitle('Various model predictions in well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MokOQhFo1Qo273wbc1qryZWgaoQ7TCLWIpKF+NSxtvzph/FJmHDkn52jaw8V3LC06BEmoZQm1SDvSj2lYlp0U1WtY2npVnYZph7pc1FbD1K82p8jvti49LiIiIiKSgUao25jmUIdplY/8ZZmXrnoNS91Wd+xgl5PH5hxNe+i8Jf1YktpqmPrV/KlfbU6R50cpoW5noxzfbaDoKMppVMr111WnYWnrNN5W9RqQpV4lf2qrYWqr5VLFtlpgG9SUDxERERGRDEoxQl3FwxJJaXk3ERERkXIrRUItIiIiFVXFqQlJaRrNsKEpHyIiIiIiGSihFhEREZFKMrM3mNkuNfc/YGY3mdllZrZD0nKUUIuIiIhIVV0J9AKY2VuB84FrgdXA3KSFaA51O9to2CLtMzW00VJvpzoNSFun8baq1wC11fyprbaG2mr+1Fabk66+Rrj7s/HtE4C57j4PmGdmC5IWooRaREREClPFS2QnVeSltCtkhJmNdPd+4HBgZs1zifNkJdQiIiJSGC2dG6alc7eK7wM/N7NngOeBewDMbE+iaR+JKKEWkeJp2awwLZslItIy7n6emd0FTAbucPfBTrcD+ETScpRQi0jxqjjXL6kscyhFpLqqOFCRcgDC3X81eNvMtgX2B55y90eTlqGEWqQZVeygksoykqp6DdMItbQ7ff/D9P1vOTN7N3AZ8CwwC/gmsBzYzczOdvdrkpSjhLqdqZMKUyclIiIi8GXg74DtgJ8Br3H3J8xsInAXoIRaRKSytEMdpqMpraGBChmeBgandpjZk+7+BIC7rzCz/qSFKKEWERERkarqMLPtiU5CHIhvD568kvjkHiXUIiIiIlJV2wEP8GIS/WDNc4kPuyihFhERkcL0rRpg2XXrig6jlPpWZZheVMXVk1KsiuTuu+Xx1kqoRUREpDC6sEuYLuwyfCihFpHiVXEkJSmtQy3tTid6hulEz2FDCbVIM5T4hSnxE5E01K+GqV8dNkqRUPet28TS+9YUHUYp9a3bVHQIIiIiLaMcIEw5wPBRioR64QvjOHThoUWHUUorXuguOgQRGY406hemUb9SUQ4Qphxg+ChFQv2qnTZw74d+W3QYpfTmP2woOgQRERmkHZUw7ahIhZUiodbhnrBMh3vU8Yep4xcREZGclCKh1uGesCyHe3rWTObG22flGE376FlzRsrtVKchaesUYM3oPbh97xtzjKZ9rBmdrm9UWw1TW22NtG31ZVMnc9FXvphzNO3hc8f/NPW2VWyradtgHkqRUItItQ2s2MDzX59fdBilNLBC077KRG01LG1bVZ2GZfn+V7Fei+wvMyXUZrYIWAtsAvrd/SAz2wH4T2A3YBHwXnd/bkvlTO3ajjlTjs4SStua1XVL0SGIiIiIDDtTfQpzemdv9vj7ODz398pjhPpt7v5Mzf1zgLvc/XwzOye+f3YO7yMiIiJtZlmXccE0HTBvZFmXzvcZLlrRgo8FDotvXwPcjRLqQqxf+yj335P/Xlg7WL+26AhEZDhSvxqmflWqLGtC7cAdZubAle4+F5jk7ksB3H2pmU1stKGZzQRmAnROmKS904Bm905r63VcZysiqp7aOt1pbMPmLCmoXvOnOm0N9av5q88BJB/qA4qTNYt9k7sviZPm/zGzPyXdME6+5wJsO3VfXaw+J7X1uvvEfXz6Wy4vOKJy+vHy5Gf519dpq2KqGtVr/lSnrVFbr5O2MdVrDpQDtIb6gOJkSqjdfUn8/woz+xFwMLDczCbHo9OTgRVDldO/rodV99+UJZS21b+up+gQREQkNmbc3migorFmBipE2k3qhNrMxgAd7r42vv13wJeAm4FTgfPj/4fMlEeOncCO049NG0pbe3b+ram37e5dzVlL0m/fzlb0ri46BKmhk5LCdFJSuaithqVtq+NWLeSw63QtikaWrSo6AkkqS68wCfiRmQ2Wc527/7eZ/Qb4gZl9BPgLcPxQBenLFJbly6TlCMO0HGG5qA8I0w+qtLu1O+7D3SfPLTqMUlr7zZlFhyAJpU6o3f0J4LUNHl8FLVjgT5qmkZQwjfqVi35Qw/SDKiJSfsq2RKRwu/Q6Zy/uLzqMUprVm+68oo6J27LNJw7KOZr20PGLbVNvO2XCNsyecUCO0bSPz31vm1Tb6fsflvb7L1tfKRJqjU6FaXRKqkDJX1iW5E/yt6TneWbfuKDoMEppSc/zqbbT0dSwLEdTq9ivFtlfqgWLNKGKHVRSSvxEJA2N+oelHfWXra8UCbVOSArTCUkiIiIi5VaOhHqPAzjsBz8vOoxSuvV47WiUydrHF3D3e7crOoxSWvt4+m11GD0s7WF0tdWwLG1VA0BhaQeABlZs4Pmvz883mDYxsGJD0SG0PTN7A7DY3ZfF9z8AHAc8Bcx292eTlFOKhFpEqk1JSpiOUpWLzvkJ0zk/MkxdCRwBYGZvJbqOyieAA4iuOvkPSQpRQt3GdOZ0WNozp3WVtLAsV0lTvYalrVfVaViWtqp+NUwrUpRLFUf+U47oj6gZhT4BmOvu84B5Zpb40GkpEuoq/tGT0uEeERERkZYZYWYj3b2f6DoqtYdaEufJpUioRUREpJq6bQlnds0uOoxSesaWFB1CFXwf+LmZPQM8D9wDYGZ7AquTFqKEWkRERKTNVHFHJc0OiLufZ2Z3AZOBO9x9cO5SB9Fc6kRKkVBrUfcwXSJbREREpDXMbDTwRmBPYKKZXeXu/e7+aDPlKIsVERGRwozs3JWdJ80pOoxS6unUyilbwTVAH9FUj3cC+wOfaraQUiTUOms6TGdNSxXoKFWYjlKJiLTU/u7+agAzuwr4dZpC9AvWxpSkhClJEZE01K+Gpe1XNagWpkG1raJv8Ia795ula8el6BXUQYUp8SuXKp7kkVSWs9H7+55m5fJZOUbTPvr7nk61nfrVsCz9qtpqWNq2KlKw15rZmvi2AdvE9w1wdx+fpJBS9LbaOw3Lsneqjj9MHb+0O33/w/T9Lxft/IVpUK313H1EHuWoBYtI4ab6FOb0zi46jFKa5emv6icyHGhQLSzLoFoV+9Ui+8tSJNTaOw3T3qmIiLQzTaULyzKVror1WuSFcJTFtrEq7p0mpVE/aXf6/odl+f6rXsPUr5ZLFdtq5UeodbgnLMvhnirunSaly7lKu9P3P0zff6mCKvYBGqEWkUqrYseflJK/clFbDVNblSrrKDoAEREREZHhTAm1iIiIiEgGmvIhIiKS0Mt23pOLPnpL0WGU0ufuPLToEEQKo4RaREQkob+sfIyPXXlM0WGU0jMrHys6BJHCKKFuYxpJCdNIioikoX41TP2qVJkS6ja2pOd5Zt+4oOgwSmlJz/OpthvZuSs7T5qTczTtoadzZuptq7healJp11VVWw3L0lbVr4apX81flrZaxXrNUl9ZlSKh1pUSw3SlRBEREZFyK0UW27+uh1X331R0GKXUv66n6BBERERaZtyqhRx2naaLNLJsVdERDG8dE7dlm08ctPkTl+f/XqVIqKd2bcecKUcXHUYpzepKP1dPV6AMy3IFSpHhQN//sCzffyV/YWmTvzHj9mb6W1qQ4bSBHy/X5dyHiyETajO7GjgGWOHur4of2wH4T2A3YBHwXnd/zswM+FfgKGAD8EF3f3Co91i/9lHuv+fwtJ+hra1fm35bTaUJ01SaclEfEJa2D9D3PyzL91/JX1ja5E/f/7AsOYBsXUl62+8C3wCurXnsHOAudz/fzM6J758NvBPYK/43nWhQfXqeAYtI+1m74z7cffLcosMopbXfLO4kG9mcdlTCNFAhVTbklRLd/RfAs3UPHwtcE9++BphR8/i1HvkVMMHMJucVrIiIiIhI2aS99Pgkd18KEP8/MX58V2Bxzeu648dERERERNpS3setGh3vaXj2h5nNBGYCdE6YxN0n/yDnUNpDs4d7a+t1p7ETdVJSQDMnJdW3VcmH6jV/qtPWUL3mTzlAMllyALXVrSttQr3czCa7+9J4SseK+PFuYFrN66YCSxoV4O5zgbkAu0/cx5X4Ndbs2ej19dqKmKqmtk63nbqv6jQnqtf8qV9NJmu/qnptrJl61fe/NVSvxUmbUN8MnAqcH/9/U83jHzez64lORlw9ODVEtr4xHd1MH3dW0WGU0piO7lTb7dG/mBt6PpNzNO1hRv/ioV8UoHoNS1uv+v6Hpf3+D26rem1M/Wr+1K82J0t9ZZVk2bzvA4cBO5lZN/BFokT6B2b2EeAvwPHxy28jWjLvMaJl8z6UJAh1UGFZOn4RERERab0hE2p3Pynw1GaLRrq7Ax/LGpSIiIiIyHChxTRFpHCdY0cw+ZDxRYdRSp3zRhQdgtRQWw1L21Y7d+xgl5PH5hxNe+i8Je1ibNVsq0X2l0qo21gVv0xJKUkRESmJjYYtSp84trWNuljOcFGKhFqJX5gSv3JRWw1TWy0XtdUwtVURqWdmrwb2je8+4u5/aGb7UiTUIiIiIiJbm5ltR7Ra3TTgIaJrqrzazP4CHOvua5KUo2MsIiIiIlJVXwbmA3u5+9+7+wxgL+A3wHlJC9EIdRvrW7eJpfcl2rGqnL51m4oOQURERIp3BPAadx8YfMDdB8zsC8DvkxZSioRaiV+YEr9yUVsNy9JWVa9h6gPKRW01LG1bVZ2GqV9tTsr66nX3zS5/6u79ZrYxaSGlSKhFRCRfVfwxTUo7KSJSY7SZHUg0d7qWAaOSFqKEuo3pLP8wneUvImmoXw1LvQ616jRIv1VbxVLgksBzy5IWUoqEWl+mMH2ZRCQN9ath6lelCqrYB6T5brv72/J471Ik1CLDRRU7qKSyJCmq1zAlfyIi5adl80REREREMtAItYiIiBRnlOO7DQz9uioa5UVHIAlphFpEREREJAMl1CIiIiIiGWjKh4iIiBRno2GLNL7X0Mb6pZGlrNSCRUREREQy0Ai1iIiIFEcnJYbppMRhQwl1G9Olh8PSXnpYdRqmyzmLSCqa8hGmKR/DhhJqERGRhLRTHaadaqky7RKKiIiIiGRQihFq7fGHaY9fqkB9QJimJ+VP/Wq5qK2GZWmrVazXIr/bpUiopTU6x45g8iHjiw6jlDrnjSg6BBEREWkTpUio1w9M5f61FxYdRimtHzij6BCkhnZSwrLspKhew9LW68IXxnHowkNzjqY9rHihO/W2aqthaqv5U1ttTpGDZaVIqKU1etZM5sbbZxUdRin1rEm3o6I6DUtbp9G2qteQtPU6tWs75kw5Oudo2sOsrltSb6u2Gqa2mj+11eZk+R3KSgm1iIiIiLSdJT3PM/vGBVvlvbTKh4iIiIhIBqUYoV7WZVwwrRShlM6yLi3qLiIiIlJmpchid+l1zl7cX3QYpTSrV5cdFREpi+7e1Zy15NaiwyilFb2riw5BpDClSKilNTTyH6aRfxGRctBvVZh+q4aPIedQm9nVZrbCzP5Q89hsM3vazBbE/46qee7zZvaYmS00syNbFbiIiIiISBkk2SX8LvAN4Nq6x7/m7hfXPmBm+wMnAq8EpgB3mtne7q7LUklb0EhKWJaRFNVrWNp6VZ2GZWmrL5s6mYu+8sUco2kfnzv+p0WHIDWq2AcUOaI/ZE27+y/MbLeE5R0LXO/uG4Enzewx4GDgl1vaSHPSwjQnTUSkPLbmMlzDzZKe54sOQaRpZvYu4CF3fyq+fy5wHPAU8Cl3fzJJOVmWzfu4mT0UTwnZPn5sV2BxzWu648dERERERMrmPGAlgJkdA5wCfBi4GbgiaSFpE+rLgT2AA4ClwFfjxxuNtTdcpsLMZprZfDObP9C7IWUYUq+2XvvX9xQdTltQnbaG6jV/qtPWUL3mT3XaGqrXVNzdBxPR9wBXufsD7v4dYOekhaRKqN19ubtvcvcB4NtE0zogGpGeVvPSqcCSQBlz3f0gdz+oo2vbNGFIA7X1OnLMhKLDaQuq09ZQveZPddoaqtf8qU5bQ/WaipnZWDPrAA4H7qp5bnTSQlLNVjezye6+NL7798DgCiA3A9eZ2SVEJyXuBfx6yCDGTmDH6cemCaXtPTtfc8tFRKR96VoUYboWxVZxKbAAWAM84u7zAczsQKJZGIkMmVCb2feBw4CdzKwb+CJwmJkdQDSdYxHwUQB3/6OZ/QB4GITEmtkAACAASURBVOgHPpZkhQ99mcL0ZRIRkXZWxdUoktI61K3n7leb2e3AROB3NU8tAz6UtJwkq3yc1ODhq7bw+vOIJnhLwbSjEqYdFRFJQ/1qWNp+VXUapt+q1jOzm4B7gfuI8uJegJqZGImUYpdwwvilzDhyTtFhlNLFdzT193yJMR3dTB93Vo7RtI8xHd2pttujfzE39Hwm52jaw4z+xUO/KED1Gpa2XvcdvYR795+dbzBt4s2jG57ak4j61bC0/arqNCxtnUI1+4CU3+1vA4cQDQa/xsz+BPwvUYJ9n7svT1JIKRJqEam2zrEjmHzI+KLDKKXOeSNSbde3bhNL71uTczTtoW+drjUmIhF3vwW4BcDMRgAHEk11vgh4BZCoE1ZCLSIiIiKVZWY7EY1SHwK8kWh1jzsZ4sKEtZRQi4iIiLSZKh6lSnP0ycz+DKwG5gG3A3PcfV2z5SihbmM6jB6W9jC66jQsbZ1Ka6ithmVpq6rXMPWr+VNbbU7K+rqaaFT6OODVwKvM7JfAb5OsVDdICbWIiIiIVJK7f2XwtpntTTTt43TgLWa20t0PTVJOKRLqKh6WSEonz4iIiIi0lpntTnTl7+lEI9Y7A08k3b4UCbWIiIhUkwbVwjSo1npm9iOiBHo10UmI/wt83d0fbqYcJdQiTVDHH5al41e9hukHtVzUVsPUVsulim01ZRv8N+B0d38my3sroRZpQhVP8khKJyWKiMhw4+4351GOEmoRKZx2VMK0o1IuaqthWuUjf1rlozlF9pcdhb2ziIiIiEgbKMUIdeeOHexy8tiiwyilzlu0zyMiIiJSZsrWREREREQyUEItIiIiIpJBKaZ89K0aYNl1TV82vRL6Vg2k37aCS+YklXZ5J9VpmJbNaw211fyprbaGls2TKitFQi0iIiLVpJ2UMO38NafInTol1G2sikvmJKWlyERERCQvSqjbWM+aydx4+6yiwyilnjVnpNpu/cBU7l97Yc7RtIf1A+nqNNpW9RqStl5Vp2Fqq62htpo/tdXmZKmvrEqRUFfxj55UkY1DRESk1bptCWd2zS46jFJ6xpYUHYIkVIqEWkRERKppqk9hTu/sosMopVmuQbXhQgm1iIiIFEYj1GEaoR4+lFCLiIhIYTRCHaYR6mz61/Ww6v6btsp7KaEWERFJaFmXccE0/XQ2sqzLig5BpDC6UqKIiIiISAbazRYREUlol17n7MX9RYdRSrN6vegQRAqjhLqN6dBkWNpDk6rTsCyHe1WvYWqr+dPUhHJRWw1Tv9qcIr/b1appESkljfqFadSvXKqYpCSlHRWpMvUKbUxJSljaJEV1GqbEr1zUVsOytFXVa5j61fypXx0+lFCLiIiItJkq7qgUuQOihFpERESkzVRxelKp51Cb2TTgWmAXYACY6+7/amY7AP8J7AYsAt7r7s+ZmQH/ChwFbAA+6O4Pbuk9qvhHT0pz0kRERERaw8zeBTzk7k/F988FjgOeAj7l7k8mKSfJOtT9wGfdfT/gjcDHzGx/4BzgLnffC7grvg/wTmCv+N9M4PLEn0pEREREZOs5D1gJYGbHAKcAHwZuBq5IWsiQw8LuvhRYGt9ea2aPALsCxwKHxS+7BrgbODt+/Fp3d+BXZjbBzCbH5YiIyFagI39hOvInIjXc3TfEt98DXOXuDwAPmNk/Ji2kqd7WzHYDDgTuByYNJsnuvtTMJsYv2xVYXLNZd/zYSxJqM5tJNILNTmMnVm7ifFLNTrCvr1fJTnXaGrX12jlhkpK/gGaSv9o6HTFuFCuXz2pVWMNaf9/TTb2+vl7P7JrdgqiGv2dsSeLX6vufTLM7f/X1KomYmY0lmqZ8OPCtmudGJy0kcQuO32we8Gl3XxNNlW780gaPbZYVuvtcYC7Aq3cZ49PHnZU0lEoZ09Hd1Otr63Xbqfu6OqnGmumkVKfJNNvx19drK2Kqmvo63XnSnIIjKqeezplNvV71mkwz9arvf2uoXlO5FFgArAEecff5AGZ2IHWDwVuSKDMws06iZPp77n5D/PDywakcZjYZWBE/3g1Mq9l8KpB8t1VEKqeKyzslpbV986e1fUVkkLtfbWa3AxOB39U8tQz4UNJykqzyYcBVRFn7JTVP3QycCpwf/39TzeMfN7PrgenA6qHmT3eOHcHkQ8YnjblSOueNSL3tHv2LuaHnMzlG0z5m9C8e+kUNqE7D0tYpwITxS5lxpEb9Grn4jnSnn4zp6EZH/hpr9shfLfUBYepX85elX9139BLu3X92fsEMA28e3fz4rZm9rubuAQ1mYPwlSTlJRqjfBLwf+L2ZLYgf+wJRIv0DM/tI/GbHx8/dRrRk3mNE81ESZ/eSL+2ohGXZUREZDvT9D9P3v1zUVsOytNW+dZtYet+aHKMpv751m9JsNh/4I/FKH7x06rIDb09SSJJVPu6l8bxoiCZv17/egY8leXNprSp+mZJK+aUTEZGc6bcqLMtvVRV3VFLugHyWaN3p54HrgR+5+7pmC9HZVSJSOP2ghmnnr1yqmKQklXY0VXUapqMprefuXwO+ZmavAE4C7jKzp4B/cfcFW976RUqo25g6qTB1/PnL0vGrXsP0gyoi0nru/qSZ3QRsQzTVeW+i1T8SUUItIiIiIpVkZrsDJxJdmHAx0bSP89z9hWbKUUItIiIiIlX1GPAQ0Wp1a4CXAf84uNpH3Qp3QUqoRURERKSqvsSLFyAcm7YQJdQiIiIiUknuPjuPcjryKEREREREpKqUUIuIiIiIZKApHyIiIlKcUY7vNlB0FOU0yod+jZSCEmoREREpzkbDFumAeUMbQxeqlrJRCxYRERERyUAj1O1Mh9HCdBhN2p2+/2H6/otIzpRQi0jxlPyFKfkrF7XVsLRtVXUapu//sKGEWkSKpzmUYWnnUKpOwzQvtVzUVsPUVocNJdRtrG/VAMuuW1d0GKXUt0qjIWXSt24TS+9bU3QYpdS3blPq7VSnjaWtU1C/uiVp+1W11bAsbVW2LiXUIk1Qxx+mjl9ERKqqFAm1kpQwJSkiIuXROXYEkw8ZX3QYpdQ5b0TRIYgUphQJ9cIXxnHowkOLDqOUVrzQnXrbx0dO4z0TLskxmvbx+MiZqbZbPzCV+9demHM07WH9wBkZtlW9hqStV9VpWJa2+qcXpvA3D8/OL5g28ucX1K/mLUtb7VkzmRtvn5VjNOXXsyZ9fWVVioRaRKptWZdxwTR1R40s60p3UpLqNCxtnYrI8LL9xuUc9/jmA4uXteC9StHbTu3ajjlTji46jFKa1XVL6m136XXOXtyfYzTtY1avliIqE7XVsLRtVXUaluX7r3oNU78qVVaKhFpERESqqduWcGbX7KLDKKVnbEnRIUhCSqhFRESkMFN9CnN6ZxcdRinN8uLmBEtztJK6iIiIiEgGGqFuY929qzlrya1Fh1FKK3pXFx2CiIiItAkl1G1MJ3uGZTnZU0SqS6unhGlFmvxpRZrhQy24jamTClPHn78sHb/qNUw/qCKSRhX71Sz9pZm9CZgNvJwoPzbA3X33JNtXq6Yrpr/vaVYur9ai7kn19z2dartxqxZy2HW6CFEjy1YVHYHU0soJYVlWTlC/Gpa2X50yYRtmzzgg52jaw+e+t03RIVTJVcD/BR4Amr5MdSkS6gnjlzLjyDlFh1FKF9+xNPW2+9gAN47akGM07WOGDaTabuJ22/LJo/bLOZr28NNrHkm97R79i7mh5zM5RtM+ZvQvTrWdvv9hab//oHrdkrT1us0zj/Ka774952jawzbPPJp6231HL+He/WfnF8ww8ObRmZYZXO3uP0m7cSkSahGpts6xI5h8yPiiwyilznkj0m2nOg1KW6eget2SLPUqUhQze11882dmdhFwA7Bx8Hl3fzBJOUqo25g6/jAlKfnTj6mIpKF+NUz96lbx1br7B9XcdiDR4ZMhE2ozmwZcC+wCDABz3f1fzWw2cDqwMn7pF9z9tnibzwMfIZqD8kl3vz1JMCKlN8rx3dIfLm5ro3TZYRERGV7c/W15lJNkhLof+Ky7P2hm44AHzOx/4ue+5u4X177YzPYHTgReCUwB7jSzvd296QnekpGSv7C0yd9GwxbpekgNbcywGoXaaph2VMpFbTVMbVWGITN7F/CQuz8V3z8XOA54imhQeFGScoZMqN19KbA0vr3WzB4Bdt3CJscC17v7RuBJM3sMOBj4ZZKARKSCtKMSlmVHRfKnthqmtirD03nAGwHM7BjgFOAk4EDgSuDIJIU0NYfazHaL3+B+4E3Ax83sA8B8olHs54iS7V/VbNbNlhNwaRV1/GHq+EVEykGj/mEa9d8a3N0Hl+55D3CVuz9ANCPjH5MWkjihNrOxwDzg0+6+xswuB75MNGH7y0STuj9MtBD2ZsE2KG8mMBNg2s6jkoYhQ1C95k912hqq1/ypTltD9Zq/+jrV4E9Ak4M/aqupWJzjbgAOB75V89zopIUkasFm1kmUTH/P3W8AcPfl7r7J3QeAbxNN64BoRHpazeZTgc0WBnT3ue5+kLsftNP4rqTxyhBUr/lTnbaG6jV/qtPWUL3mT3XaGqrXVC4FFhDNtnjE3ecDmNmBxFOekxgyoTYzI7p6zCPufknN45NrXvb3wB/i2zcDJ5rZKDN7BbAX8OukAYmIiIiIbA3ufjVwKNHqdEfVPLUM+FDScpJM+XgT8H7g92a2IH7sC8BJZnYA0XSORcBH48D+aGY/AB4mWiHkY1rhQ0RERETKxsz2dfc/mdkk4IBoHPkl/pKknCSrfNxL43nRt21hm/OIzpoUERERESmrzxJdV6X+Ai+Q54VdRERERETakbufHv+f6QIvSqhFRESkOFo2L0zL5rWcmb0BWOzuy+L7H+DFC7vMdvdnk5SjdWpEREREpKquBHoBzOytwPnAtcBqYG7SQjRCLdKEvnWbWHrfmqLDKKW+denPPVa9hmWpV8mf2mpY2rbat2qAZdetyzma9tC3SiP3W8GImlHoE4C57j4PmFezGMeQSpFQq4MK04+piKShfjVM/Wq5dO7YwS4njy06jFLqvCX9RIIq9gEpv9sjzGyku/cTXdhlZs1zifPkUiTUC18Yx6ELDy06jFJa8UJ30SFIjc6xI5h8yPiiwyilznkj0m+reg1KW6+q0zC11dZIW68aoQ7TCPVW8X3g52b2DPA8cA+Ame1JNO0jkVIk1FO7tmPOlKOLDqOUZnXdUnQIIi1XxZGUpNKOpvasmcyNt8/KOZr20LPmjKJDkBraSQnTzl9z0tSXu59nZncBk4E73H3wTNAO4BNJyylFQi2toR/UsLQ/qKrTsCxJyvqBqdy/9sIco2kf6weU/JWJ+oAw9av5y9KvVrFe09aXu/+qwWOPNlOGVvkQEREREclACbWIiIiISAZKqEVEREREMijFHOru3tWcteTWosMopRW9iU8w3cyyLuOCaaX4E5fOsi5LvZ3qtLG0dTq4req1MbXV/Kmttobaav7UVpuTpb6yqlZNi4iIiFTALr3O2Yv7iw5jq5rVW9yl2pVQt7H+vqdZubxaZ/gm1d/3dOrtVKeNpa1TaQ211bAsbVX1GqY+oFw0Qr11laKmR46dwI7Tjy06jFJ6dn76qTD72AA3jtqQYzTtY4alWyz/VTvBPR/SqQeNvOV/0m+77+gl3Lv/7NxiaSdvHr0k1XZqq2FZ2qr61bC0/eq+2z3NPUedm3M07eEt/5F+J6WK9ZqlvrIqRUK9R/9ibuj5TNFhlNKM/sVFhyA1dEWvsCxX9NKFXcLSXthFbTUsS1vVZbLDUl8me6Nhi7Tz19DG9COuVewDiryyZCkSahERkeGgiklKUmmTGe1Qh6XdoZbIxO225ZNH7bfZ45f94YHc30sJtUgTNDoVlnp0SmQYUR8QlrYPUJ2GqV8dPpRQi0jh9IMaph9UaXua8hGWYcpHFfvVIvtLtWARERERkQxKMUK9YvUGLrst//ks7WBF+uu6iIiIiMhWUIqEesy4vZn+lsuLDqOUfrz8jKJDkBprbE/u6Lq56DBKaY0dmnrbnpVTuOnKai3vlFTPynR9wPqBqdy/9sKco2kP6wfS96tqq2Fp22rPmsnceLvW9m6kZ036tlrF36ssv0NZlSKhFhku/rLyMT525TFFh1FKz6x8LPW2HRO3ZZtPHJRjNO2j4xfbFh2C1Oi2JZzZNbvoMErpGUu3Znp372rOWpL+mgvtbEVv+sPUVfy9yvI7lJUS6jamEaqwLCNUIiIi0h7M7F3AQ+7+VHz/XOA44CngU+7+ZJJydFKiiIiIiFTVecBKADM7BjgF+DBwM3BF0kKUUIuIiIhIVbm7b4hvvwe4yt0fcPfvADsnLURTPkSkcAMrNvD81+cXHUYpDazYMPSLREQkLTOzscAG4HDgWzXPjU5aiBJqERERkTbzsp335KKP3lJ0GFvV5+5MtcrHpcACYA3wiLvPBzCzA4GlSQtRQi0ihdPKCWFpV04QGTZGrWdgt98UHUU5jVqfelOt8pGMu19tZrcDE4Hf1Ty1DPhQ0nKUULexMR3dTB93VtFhlNKYju6iQxBpKe2khGknRUQGmdm+7v4nM5sEHGC22eXe/5KknCETajMbDfwCGBW//ofu/kUzewVwPbAD8CDwfnfvNbNRwLXA64FVwAnuvijZxxIRkTxM9SnM6Z1ddBilNMu1bGaZqK2Gqa1uFZ8FTge+2uA5B96epJAkI9Qbgbe7+zoz6wTuNbOfAJ8Bvubu15vZFcBHgMvj/59z9z3N7ETgAuCEJMFIvrQOdVjadajV8Ydl6fhHdu7KzpPm5BhN++jpnFl0CFJDfUBY2j5gWZdxwTQdMG9kWddmo6WSM3c/Pf7/bVnKGbIFu7sD6+K7nfG/wYz95Pjxa4DZRAn1sfFtgB8C3zAzi8sREdnMuFULOey64i4ZW2bLVhUdgUhr7dLrnL24v+gwSmlWr1KnVjOzNwCL3X1ZfP8DvHhhl9nu/mySchLtEprZCOABYE/gm8DjQI+7D34DuoFd49u7AosB3L3fzFYDOwLPJHkvEameMeP2ZvpbLi86jFL68XId8pX21jFxW7b5xEFFh1FKHb/YtugQquBK4AgAM3srcD7wCeAAYC7wD0kKSZRQu/smoonaE4AfAfs1eln8f6PjE5vtYpnZTGAmwE5jJyYJQxKordcp47t0UmJAMyclqq22Rm29jhg3SifQBTRzAp3aamuoXvNX//2v2moUSTW7akV9vUoiI2pGoU8A5rr7PGCemS1IWkhTk5bcvcfM7gbeCEwws5HxKPVUYLDX7wamAd1mNhLYDthsuNzd5xJl/uw+cR8d08hJbb2+epcxqtccqK22Rm29dk0ap3rNgdpqa9S3Ve38NdbMzp++/62hek1lRE0+ezjxDkkscZ6cZJWPnYG+OJnehmhY/ALgZ0TD4NcDpwI3xZvcHN//Zfz8TzV/WkS2RCd6heksfxGRlvo+8HMzewZ4HrgHwMz2BFYnLSRJ5j0ZuCaeR90B/MDdbzGzh4HrzWwO8Fvgqvj1VwH/bmaPEY1Mn5g0GBGpJq2ZHKY1k0VEWsfdzzOzu4jy3TtqBoE7iOZSJ5JklY+HgAMbPP4EcHCDx18Ajk8agMhwopNnwnTyjIiIDDdm9h53vyG+vT2wHsDdH22mnI4WxCYiIiIiMhzMqrl9V9pCSrGSenfvas5acmvRYZTSit7E03dkKxhYsYHnvz6/6DBKaWDFhqJDEBERaZYFbjelFAm1iFSbTkoM00mJIiIttY2ZHUg0a2N0fPuvibW7P5ikECXUIiIiSW0cQ8eiNxQdRTltbGrKqUhZLAUuiW8vq7kNL14ZfEhKqNvY+oGp3L/2wqLDKKX1Axr1KxOt8hGmVT5ERFrH3d+WRzmlSKindm3HnClHFx1GKc3quqXoEERaTlM+wjTlo1xGjp3AjtOPLTqMUnp2vs6FkuoqRUItrbGsy7hgmv7EjSzrSnfegUZSwzSSKlXQ3/c0K5fPGvqFFdTf93TRIYgUphTZlhK/sLSJn7TGyM5d2XnSnKLDKKWezplDv0i2GvWrYVn6VfUBYeoDpMrU24qIiEhhtl+zjuPvuqfoMErp+jVFRyBJlSKh3qXXOXtxf9FhlNKsXh/6RQHjVi3ksOsOzTGa9rFsVbrt1FbDsrRVyZ/aapj61dZI26+OGbc3099yeb7BtIkfL9c5FMNFKRLqMR3dTB93VtFhlNKYju7U2+666zi+MkeXyW7kns+muziL2mpYlraqeg1LW6+q07AsbXXidtvyyaP2yzGa9vHTax5Jtd2E8UuZcaSm0TRy8R1LU2+7jw1w46hqXXBrhg0U9t669LiIiIiISAalGKGW1uhbt4ml92kCViN96zYVHYKIiKDfqi3Rb9XwoYRaRArXOXYEkw8ZX3QYpdQ5b0TRIYiIDEtb87dFCXUb05USw3SlxHLRCFVY2hEq7aSEZdlJUb2GaedPqkwJtYhIG9JOSpgOo4tI3pRQtzGd5R+W5Sx/keFAI6lhWUZStaMSpqMp+dOo//ChhLqNqZMKUyclIiIieVFC3cY0khKmQ77S7vT9D9P3X0TypoRaRERECqOdvzDt/A0furCLiIiIiEjMzLY3M2tmGyXUIiIiIlJJZnaume0b3x5lZj8DHgeWm9kRScspxZQPnTwXppPnRERERFrmBODL8e1T4/93BvYGrgHuTFJIKRJqERERqSYNqoVpUG2r6HV3j28fCVzv7puAR8wscZ5cioRaJySE6YSEclHHH6arz7WGflDLRW01TG21XKrYVlO2wY1m9ipgOfA24Mya57ZNWkgpEuoq/tGTUgclIiIi0jKfBn5INM3ja+7+JICZHQX8NmkhpUiopTW0oxKmHRVpd/r+h+n7LyKD3P1XwL4NHr8NuC1pOUqoRaRwmvYVlnbal+o0TFPpRGSQmZ3i7v9hZp9p9Ly7X5KkHCXUIlI4jaaGpR1NVZ2GaYRaRGqMif8fl6UQJdQiIiIiUknufmX8/z9nKUcXdhERERGRSjKz081sr/i2mdnVZrbazB4yswOTljNkQm1mo83s12b2OzP7o5n9c/z4d83sSTNbEP87oCaYy8zssTiY16X9kCIiIiIiLfQpYFF8+yTgtcDuwGeAy5IWkmTKx0bg7e6+zsw6gXvN7Cfxc59z9x/Wvf6dwF7xv+nA5fH/IiIiIiJl0u/uffHtY4Br3X0VcKeZXZi0kCFHqD2yLr7bGf/zLWxybByMx0uRTDCzyUkDEhERERHZSgbMbLKZjQYO56WXGt8maSGJTko0sxHAA8CewDfd/X4zOwM4z8zOBe4CznH3jcCuwOKazbvjx5bWlTkTmAkwbedRSeOVIdTW65TxXVo2K6CZZbPUVltD9Zo/ff+TaXbZPNVrMupXi6d6TeVcYD4wArjZ3f8IYGaHAk8kLSRRQh1f0/wAM5sA/Ci+ROPngWVAFzAXOBv4EmCNimhQ5tx4O1635/gtjXhLE2rr9dW7jFG95qC+TvVj2lizSYrqNZlm6rW+X9WyeY01u2ye+tX86fufTJZ+VblVMu5+i5m9HBjn7s/VPPUb4ISk5TS1yoe79wB3A+9w96XxtI6NwL8BB8cv6wam1Ww2FVjSzPuIiIiIiLSamZ0CnFSXTAO8D3h30nKGHKE2s52BPnfvMbNtgCOAC8xssrsvNTMDZgB/iDe5Gfi4mV1PdDLiandf2rBwaan1A1O5f23i+fSVsn7gjKJDEGkpXSkxTFdKLBddhChMFyHaKj4LvLXB49cTDSJfl6SQJFM+JgPXxPOoO4AfxMPjP42TbQMWAP8nfv1twFHAY8AG4ENDvYE6/jB1/CIiIiItM8Ld19Y/6O5r49XtEhkyoXb3h4DNFrZ297cHXu/Ax5IGABpJ3ZIsI6kTxi9lxpFzcoymfVx8hw6aSHtTvxqmI1TlokG1MA2qbRWdZjbG3dfXPmhm44jOE0xElx5vYz1rJnPj7bOKDqOUetak+0FVkhKWJUlRvYYp+SsXtdWwtG318ZHTeM+ES3KOpj08PnJm6m2rmAOk/G2/CvihmZ3h7osAzGw34Jvxc4kooRYRERGRSnL3i81sHfBzMxtLtDLdeuB8d788aTlKqNvYsi7jgmn6EzeyrKvR6o5SFLXVMLVVEZHWcvcrgCvihNoazakein7B2tguvc7Zi/uLDqOUZvVqec4yUVsNU1uVdqfvf5i+/1tXzZXBm9bUOtQiIiIiIvJSGqFuYzqMHqbD6NLu9P0Py/L9V72GqV+VKitFr9Ddu5qzltxadBiltKJ3ddEhSA39mIbpx1REpDyq+HtV5O9QtWpaREQkA833DUs737eKiV9SGqgYPtSCRZqgH9MwnTxTLv3relh1/01Fh1FK/et6ig5BRNqMEuo2ph/UMP2gSrub2rUdc6YcXXQYpTSr65aiQ5AaGqgIyzJQUcV63ay+Rjm+28BWeW8l1CJSOJ1HEZb2PArVaViWc1NUr2Fqq/nTeVTDRykS6u03Lue4x3XZ0Uau31h0BFJLc/3CNNdPqmDk2AnsOP3YosMopWfnKykukyr+XumkRBGpNE1PCNP0BBFJo4rTPouczlmKhPq5UZOYt8f7ig6jlJ5b/L3U22okJUwjKeVSxZGUpNKOuOj7H6bvf7morYZlaatVrNciv9ul+AWr4h89KXX8UgVVHElJSifQSrur4slzSWn1pOGjFAm1fkzDsvyYql7DlKSUi3aqw7RTXS7qV8PS9qs6KTFMJyUOH6VIqEWk2pSkhKVNUlSnYVl2qLXzF6adP6kyJdQiTVCSEqZRf6kC9QFhafsAnZQcppOShw8l1G1MIylhaUdSVKdhOnmmNdRW86e22hpp61UnJYdpOdLhQy1YREREpM1U8WTPIk/iVEItIiIi0maqOPJf5Ih+R2HvLCIiIiJSMma2vZk1lZ0roRYRERGRSjKzc81s3/j2KDP7GfA4sNzMjkhajhJq5RmcvQAAEf5JREFUEREREamqE4CF8e1T4/93Bg4F/iVpIUqoRURERKSqet198GzGI4Hr3X2Tuz9CE+caVmu2esVovdQwrZks7U7f/zBdgbY10tZrFVejSEqXHt8qNprZq4DlwNuAM2ue2zZpIUqoRZqgH9Mw7aSISBpVXI0iKa1DvVV8Gvgh0TSPr7n7kwBmdhTw26SFqAWLSOG0oxKmq8/lT1efKxd9/8OyDFRUceQ/zYi+u/8K2LfB47cBtyUtpxQJta48FZblil7bb1zOcY9fkmM07eP6jem2U52Gpa1TUB+wJWn7gPVrH+X+ew7POZr2sH5t+m3VVsPStlX1q2FZ+lVJxszeBTzk7k/F988FjgOeAj41OGI9lFIk1CLDxXOjJjFvj/cVHUYpPbf4e6m3HbdqIYddd2iO0bSPZauKjkBEpK2dB7wRwMyOAU4BTgIOBK4gOlFxSIkTajMbAcwHnnb3Y8zsFcD1wA7Ag8D73b3XzEYB1wKvB1YBJ7j7oi2VXcXDEknphASpgrU77sPdJ88tOoxSWvvNmUWHIDU0PSEs7fQEDVSEZRmoqOLc9JRzzt3dN8S33wNc5e4PAA+Y2T8mLaSZmv4U8AgwPr5/AdHk7evN7ArgI8Dl8f/PufueZnZi/LoTmngfERHJaMy4vZn+lsuLDqOUfrz8jNTbam56mOaml0sVBytTDkKamY0FNgCHA9+qeW500kISJdRmNhU4mmhY/DPx5RjfDpwcv+QaYDZRQn1sfBuisya/YWZWs8afbCUa9QtLO+qnuX5hmutXLppDHZZlDnUVR/2SSrsihealh2U5j6qKbTVlG7wUWACsAR5x9/kAZnYgsDRpIUlr+lLgLGBcfH9HoMfdB3d9uoFd49u7AosB3L3fzFbHr3+mtkAzmwnMBNhp7MSk8coQ6uu1anunSTWzF1tbp+M6WxVR9dTXq+ZQN9bMHOr67//0t3y/RVENb82OUNfW64htxmnKR0AzUz70W5VMsyOutfXaOWFSK0JqO+5+tZndDkwEflfz1DLgQ0nLGTKhjidor3D3B8zssMGHG8WU4LkXH3CfC8wF2H3iPhq9zkltvU7axlwjVI01M0JVX6ctCqlyVK/50/c/mWZHqOvrVUepGmvmKFVtnXZNGudnds1uTVDD3DO2pKnX19brtlP3Vb+akLs/DTxd91ji0WlINkL9JuDd8QLXo4nmUF8KTDCzkfEo9VRg8K/eDUwDus1sJLAd8OyW3kCHJsOyHJqcuN22fPKo/fILpo389JpHig5BRIYhnUAXlvoEuo1j6Fj0hnyDaRcbH029aRVXTypyVaQhE2p3/zzweYB4hPpMd3+fmf0X8A9EK32cCgweA7s5vv/L+Pmfav60iIi0A51HEZb2PArNoQ7LModatq4ss9XPBq43szlEl2a8Kn78KuDfzewxopHpE7OFKGmtWL2By257oOgwSmnF6qIjEBERgP6+p1m5fFbRYZRSf9/TQ78ooIoLExS5zGhTCbW73w3cHd9+Aji4wWteAI7PITYRERERkdKr1noqFVPFvdOk0u7Fqk7DsowMqF7DdGGXclFbDUvdVjWHOizDHGrZupRQt7EqnpCQlC7nLCJpqF8NU78qVdZRdAAiIiIiIsOZRqjbmA5NhukwurQ7ff/D9P0XkbwpoRYREUlIOyphaXdUpnZtx5wpR+ccTXuY1XVL0SFIQkqoRUREpDDLuowLpikdaWRZV6OLT0sZWRmuuWJmK4H1wDNFxxLbifLE8nJ33znNhma2FliYczxZDPt6VVvdIrXV1kjbVlWnYWqrraG2mr/h2laLqsOX1JeZ/XccS71n3P0deb5xKRJqADOb7+4HFR0HlCuWLMr2OcoWT1pl+hxliiWLsn2OssWTRtk+Q9niSatsn6Ns8aRRts9QtnjSKvJztEsdNkOrfIiIiIiIZKCEWkREREQkgzIl1GU6bbpMsWRRts9RtnjSKtPnKFMsWZTtc5QtnjTK9hnKFk9aZfscZYsnjbJ9hrLFk1aRn6Nd6jCx0syhFhEREREZjso0Qi0iIiIiMuwooRYRERERyUAJtYiIiIhIBkqoRUREREQyUEItIiIiIpJB2ybUZrbJzBbU/NstRRlfMrMj8o9ORERERNpF2ybUwPPufkDNv0XNFuDu57r7nS2IrZTM7P+Z2R/N7KF4J+QnZvaVutccYGaPxLcXmdk9dc8vMLM/bOE9DjOz1Wb2WzN7xMy+mDHm2WZ2ZpYyWs3MJpnZdWb2hJk9YGa/NLO/31JdmNnBZvYLM1toZn8ys++Y2baB8j9oZivjun/YzE7PGO93zewfspSxNdTsNP/BzH5sZhPix3czs+frdqi74ufeaWbz4/r+k5ldvIXyZ5vZ0zXv8e6M8d5tZqW9FK+Z/f/2zj1IquKKw99PpRSFmPimjJqAQomoIEHE9wMf8QloAioVNJav4AsV34maFAaDigoaiRY+koCIkYgxkdWggBGjBlYoNErwVVZSKkGNgIrIL390D1yGmd3ZmWXZne2vaqtmTvd033tu3+5zTp9715JuzXy/XNIN8fMNkpZL2i5TvjTzeQdJD0taFMfgnyV1LtJP9vq8JukeSWWvRXH8jy339+ubPD0dK2mhpJ1L0GnR61Gkn1Y1XrNk5oIFkl6VdKmkjSQdnZkDlsb5tFbSQ0XaaXXrU47s2MvIsmPqNUmn1tPGA5LejvXnSOpT4TG9I2mbStpoSqrZoF6HOJHPihd6jqT9M2VXSJofb8aRUbbasJDUU9IMBYNomqQOUX5RHGjzJD28Yc6scuLAPx7Yx/ZeQF9gJDAwr+ogYELme3tJO8U2di+xu1m2ewDfAwZL6pl3LJuUcQrNEkkC/gjMtN3Rdk+CDr8dq6yjC0nbA5OBK213AXYHngLa19HVJNvdgUOBm2Ib2eOoGp1myDnN3YAlwNBM2aI8h3qFpG7AWGCw7d2BbsBb9fQxOur1B8D4fMOvyvT6JTCgjgVsMXBZvjCO8SnAc7Y72e4KXANsn183w6Ko172ArkC/vDY3LuP4mzWSjgDGAMfYfi+KC+o0Ut/1KERrGq9ZcnPBHsCRwLHA9ban5eYA4BXg9Pj9R3W01WrWpxLJjamTgHGS2tRTf3isfxUwLr+wmvVXzQZ124xnOiXKPgSOtL0PwVC8E0LUijCh97a9N/CrbENxAI0BTokG0XhgRCy+CugRjdDz1vdJrUc6AIttfwlge7HtGcAnknpn6v0QyDoOj7DG6D4VmFhqh7aXAf8AOsUo02RJTwA1AJKGS3o5Ois35n6nEEl/Q9IzQJeGn2qTcjiwwvY9OYHtd22PyVbK6oJgGD5oe3Yss+1HbX9QX2e2PwQWAbvE6MJvJNUAD0naWNKojE7PhWAQSRobHcMnge3q6qOZMhvYsZ46VwAjbP8TwPZK23eX0rjt14GVwDbR0b5N0rPAzZK2kDQ+6nWupJMAJLVViNrOkzQJaFv22TUNKwn/3WxYkfLxwEBJW+XJDwO+yhvjtbZnUQ+2VwIvALvG6OCzkiYA8wEkDZb0UpzHx+UMbUlnSnpT0gzggIaeaFMj6SDgXuA424syRcV0CvVfj6K0kvFakDgHngNcEJ29cttpDetTydheCCwHvlXiT2YCu8Lq3Y6b4v16saRtJf0h6u9lSQfEeltLqonjchxQ9vXbEFSzQZ1N+egfZW2AeyXNJ0QAu0Z5X+B+28sBbC/Ja6sLIZr1tKRa4DrWRBjnAb+XNJgwgbVUaoCd4iJ1t6RDonwiIaKKpP2A/8YbK8ejwID4+QTgiVI7lLQ1sB+wIIr6AENsHy7pKGA3YF+gO9BT0sExWjAI6BH77dXwU21S9gDm1FcpTxfdCBN5g5HUEegI/CuKegIn2T4NOAv41HYvgt7OlvRdoD9hjO8JnA3sv07DzZhoZB0BTM2IO2Uc6ruirBK99gZWAR9FUWegr+3LgGuB6VGvhwGjJG0BnA8sj872CMK1aO7cBZwuacsCZUsJBuDFefJK9Lo54drNj6J9gWttd1XY8RoIHBAjXl/HY+sA3EgwpI9kzTzeXNkUeBzol3PmMhTTaY66rkdRWtF4LYjttwj2TdnBgVayPpWMpH2AhdFhKYUTWHNfA3zT9iG2bwXuIES+ewEnA/fFOtcDz8cdgqnAzo1z9E1D1YbeizAM+ADYm3CzfRHlAur6H+wCFtgulA90HHAwcCLwU0l7xKhLi8L20jgZHESYZCdJuooQjX5B0mWEiSI/Ar0E+FjSIOB1ggdbHwdJmkuY8EfaXiCpF/B0xpk5Kv7Njd/bESaw9sCUnPMjaSotiGjcHQisAIZTWBflND1Q0oGEbeJzbS+J7Uy1/XmscxSwl9bkR29J0OnBwETbXwP/ljS9zNNratpGB/c7BIPu6UxZLqWgUoZFZ/kzYKBtR71OjvqCoNcTtSZXcjPCQnAwcRfM9jxJ8xrheNYrtv+nkF96EfB5gSp3ArXK5PaWSad47Qw8bvsvkg4FXrL9dqxzBMGoeznqvC1hl7E3Ib3kI4AYTS2Yr91M+IoQhT+LwoZzUZ2WcD3yaVXjtR7KjW622vWpCMMUnsvpCBxTQv1Rkq4jOHNnZeSTMp/7Al0za903JLUnjMEBALaflPRxpQfflLQ2g3pL4H3bqyQNAXJ5ejXAzyRNsL1c0lZ5Ueo3gG0l9bE9WyEFpDPBgNzJ9rOSngdOI9xYnzTdKTUeccJ9DnguRvGH2H5A0jvAIQRPspBTMYkQSTmjxK5m2T6+gHxZ5rOAX9peKwdL0iXU7fw0NxYQ9AaA7aEKOZGvRFEhXSwgGBKPN6CfSbYvKCDP1+mFtqdlK0g6lpal0xyf2+4eo3d/IqTK3FlH/ZxeX21AH6NtF3pwMV+vJ9t+I1shLhYtUa+3E3ZV7s8vsP2JQkrGTzLiBUBDH2It5vDk6/VB21dnK0jqR8vS6ypCqtwzkq6xfVO2sIhOsxS9HgVojeN1HeJO3dcEB6yhtKb1qRRG275F0gBC6mAn21/UUX+47UcLyLP62wjokwn2AC1/DFZzykch7gaGSHqRYBAvA7D9FGF74ZUYNVnrqVzbKwgLxs2SXgVqCdviGwO/i8bnXMLAa5HGtKQuknbLiLoD78bPE4HRhEXw/QI/n0LIO59WoKxcpgE/ltQuHt+OCk/DzwT6K+T7tSdsKzVnpgObSTo/Iyv4to4MYwnjdHXuukIu6Q4VHss04PzoECKpc9zqnQkMUsix7kDYoWgx2P6UEMG7XHU/MDMKuEbx7RMKbwG4tBEOYRpwoeJqIKlHlM8ETo+yboQH8Jo9MZjwCGtHl7LcBpzLmoDMdGBTZd4uI6lXJm2sXP4KnBLveyRtJWkX4O/AoQr5lm0ID+A1a2LE8nhC+kYhvebrNPvb+q5HQ6mq8ZqPpG2Be4CxtteXcVYt61PJ2H6MEAga0gjN1QCrA0CScs51dgx+n9LztZsFVRuhtt2ugGwha08SV2fKRhLeapGtf0bmcy1hOyKfAys91mZCO2CMwqvHVhJycM+JZZMJOU8XFvqh7c+Am2G1h1kxtmtiDuXs2OZSwtsZ5sQt3lqCwV/vg08bkrjl2g8YLekKwjbYMuDKOn7zQUyhuSVO0qsIE81jFR7OfYT0iDlxMf2I8DDuFMLDk/OBN4EZFfbT5NieG53dQRQZE3Eb+xJgokLuroEnG6H7XxCiiPOiXt8hGE+/Bu6PW+e1wEuN0FdTcSuZBS+L7cUKD3oPi98tqT9wu0Ka2BcEHVxSyQHYfi1uHdcovK3iK2Co7RcVXh83G/gPIXrb7N8KEtOwjgFmSlqcV7aWTgtQ9HqUQTWO11z6VxvC+vVbgpOyXqiW9SmPzSVlA2aF9PdzYIKke22vqqCvi4C74ljbhLC+nUd4NmKipDmEdei94k00P7T+HLhEIpFIJBKJRKL6aW0pH4lEIpFIJBKJRKNStSkfiQ2LpKOJaSAZ3s68wjDRQCSdybpvCfib7aGF6idKQ9K1rJuHO9n2iEL1E6UhaU/C1nuWL233LlQ/URppvFZOWp8qQ+FtVfnvf7/DdikPzlYtKeUjkUgkEolEIpGogJTykUgkEolEIpFIVEAyqBOJRCKRSCQSiQpIBnUikUgkEolEIlEByaBOJBKJRCKRSCQqIBnUiUQikUgkEolEBfwfEHULmG+CqUoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_all_facies(blind,'SVM_Pred','GPC_Pred','RFC_Pred', 'NNC_Pred', 'KNN_Pred','DT_Pred', 'LR_Pred', facies_colors)\n",
    "plt.savefig(\"Compo.png\", dpi=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclustion:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. SVM, NNC, and KNN show better performance in test data(table E1).<br>\n",
    "2. when it comes to examining models with new data(blind well) all model performance drops (table E2). <br>\n",
    "3. This can be attributed to data shortage. If we look at carefully the last cross-section showing various model outputs compared with real facies distribution, SS,  CSiS, and FSiS correlate appropriately. PS has the next better agreement with real data. BS, and D show the weakest correlation with real facies distribution. <br>\n",
    "4. The trend mentioned above (3), is in agreement with data sample frequency which showed at the first step of data wrangling in the bar chart. Facies are predicted better if they have enough samples in the training data set.<br> \n",
    "5. To improve the results we can work on two important aspects: 1.introduce more data sample to models, 2. optimize model parametrers in detail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Amato del Monte, A., 2015. Seismic Petrophysics: Part 1, *The Leading Edge*, 34 (4). [doi:10.1190/tle34040440.1](http://dx.doi.org/10.1190/tle34040440.1)\n",
    "\n",
    "Bohling, G. C., and M. K. Dubois, 2003. An Integrated Application of Neural Network and Markov Chain Techniques to Prediction of Lithofacies from Well Logs, *KGS Open-File Report* 2003-50, 6 pp. [pdf](http://www.kgs.ku.edu/PRS/publication/2003/ofr2003-50.pdf)\n",
    "\n",
    "Dubois, M. K., G. C. Bohling, and S. Chakrabarti, 2007, Comparison of four approaches to a rock facies classification problem, *Computers & Geosciences*, 33 (5), 599-617 pp. [doi:10.1016/j.cageo.2006.08.011](http://dx.doi.org/10.1016/j.cageo.2006.08.011)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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 "nbformat_minor": 1
}
